AU2019351130B2

AU2019351130B2 - Methylation markers and targeted methylation probe panel

Info

Publication number: AU2019351130B2
Application number: AU2019351130A
Authority: AU
Inventors: John F. BEAUSANG; Samuel S. Gross; Arash Jamshidi; Seyedmehdi SHOJAEE; Oliver Claude VENN
Original assignee: Grail Inc
Current assignee: Grail Inc
Priority date: 2018-09-27
Filing date: 2019-09-27
Publication date: 2025-10-23
Anticipated expiration: 2039-09-27
Also published as: EP3856903A1; US20240084396A1; US20210238694A1; EP3856903A4; US12410482B2; US20230140937A1; US11685958B2; CN113286881A; US11795513B2; CA3111887A1; WO2020069350A1; US20220380857A1; IL324427A; US11725251B2; US11410750B2; US20210238693A1; AU2019351130A1; IL281741A; IL281741B1

Abstract

The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein are methods of designing, making, and using the cancer assay panel for the diagnosis of cancer.

Description

PCT/US2019/053509

METHYLATION MARKERS AND TARGETED METHYLATION PROBE PANEL

1. CROSS-REFERENCE CROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No.

62/737,836, filed September 27, 2018 and International Appl. No.

PCT/US2019/025358, filed on April 2, 2019, both of which are hereby

incorporated by reference in their entireties.

2. SEQUENCE LISTING

[0002] The instant application contains a Sequence Listing which has been

submitted electronically in ASCII format and is hereby incorporated by reference

in its entirety. Said ASCII copy, created on September 26, 2019, is named 50251-

846_601_SL.txt and is 52,371,626 bytes in size.

3. BACKGROUND

[0003] DNA methylation plays an important role in regulating gene expression.

Aberrant DNA methylation has been implicated in many disease processes,

including cancer. DNA methylation profiling using methylation sequencing (e.g.,

whole genome bisulfite sequencing (WGBS)) is increasingly recognized as a

valuable diagnostic tool for detection, diagnosis, and/or monitoring of cancer. For

example, specific patterns of differentially methylated regions may be useful as

molecular markers for various diseases.

[0004] However, WGBS is not currently suitable for a product assay. The reason is

that the vast majority of the genome is either not differentially methylated in

cancer, or the local CpG density is too low to provide a robust signal. Only a few

percent of the genome is likely to be useful in classification. With WGBS, deep

sequencing (up to ~1000x) can be done in only a small set of genomic regions,

perhaps 0.1% of the genome at current sequencing costs, because of cost

constraints.

[0005] Furthermore, there have been various challenges in identifying

differentially methylated regions in various diseases. First off, determining differentially methylated regions in a disease group only holds weight in comparison with a group of control subjects, such that if the control group is small in number, the determination loses confidence with the small control group.

Additionally, among a group of control subjects, methylation status can vary which

can be difficult to account for when determining the regions that are differentially

methylated in a disease group. On another note, methylation of a cytosine at a CpG

site is strongly correlated with methylation at a subsequent CpG site. To

encapsulate this dependency is a challenge in itself.

[0006] Accordingly, a cost-effective method of accurately diagnosing a disease by

analyzing DNA from differentially methylated regions has not yet been available.

4. SUMMARY

[0007] Early detection of cancer in subjects is important as it allows for earlier

treatment and therefore a greater chance for survival. Targeted detection of cancer-

specific methylation patterns using cell-free DNA (cfDNA) fragments can make

early early detection detectionof of cancer possible cancer by providing possible cost-effective by providing and non-invasive cost-effective and non-invasive

method for getting information relevant to detecting the presence or absence of

cancer, a cancer tissue of origin, or cancer type. By using a targeted genomic

region panel rather than sequencing all nucleic acids in a test sample, also known

as "whole genome sequencing," the method can increase sequencing depth of the

target regions.

[0008] Towards that end, the present description provides cancer assay panels

(alternatively referred to as "bait sets") for detection of cancer-specific methylation

patterns in targeted genomic regions, along with methods of using the cancer assay

panels for diagnosis of cancer. Further provided herein are methods of designing

and making the cancer assay panel by identifying genomic sites having cancer-

specific methylation patterns as well as a list of genomic sites or genomic regions

that can be used for various methods provided herein. The methods described

herein further include methods of designing probes to enrich for cfDNA

corresponding to or derived from selected genomic regions efficiently without

pulling down an excessive amount of undesired DNA.

[0009] In one aspect, provided herein is a bait set for hybridization capture, the bait

set comprising a plurality of different oligonucleotide-containing probes, wherein each of the oligonucleotide-containing probes comprises a sequence of at least 30 bases in length that is complementary to either: (1) a sequence of a genomic region; or (2) a sequence that varies from the sequence of (1) only by one or more transitions, wherein each respective transition of the one or more transitions occurs at a cytosine in the genomic region, and wherein each probe of the different oligonucleotide-containing probes is complementary to a sequence corresponding to a CpG site that is differentially methylated in cancer samples relative to non- cancer samples.

[0010] The bait set can comprise at least 500, 1,000, 2,000, 2,500, 5,000, 6,000,

7,500, 10,000, 15,000, 20,000, 25,000, 50,000 or 100,000 different

oligonucleotide-containing probes. In one aspect, the CpG site is considered to be

differentially methylated in cancer samples relative to non-cancer samples based

on criteria comprising a number of cancer samples that comprise an anomalously

methylated cfDNA fragment that overlaps the CpG site. In one aspect, the CpG site

is considered to be differentially methylated in cancer samples relative to non-

cancer samples based on criteria comprising Ncancer and Nnon-cancer, wherein: Ncancer

is a number of cancer samples that include a cfDNA fragment covering the CpG

site that (1) has at least X CpG sites, wherein at least Y% of the CpG sites are

methylated or unmethylated, wherein X is at least 4 and Y is at least 70, and (2) has

a p-value rarity in non-cancerous samples of below a threshold value; and Nnon-cancer

is a number of cancer samples that include a cfDNA fragment covering the CpG

site that (1) has at least M CpG sites, wherein at least N% of the sites are

methylated or unmethylated, wherein M is at least 4 and N is at least 70, and (2)

has a p-value rarity in non-cancerous samples of below a threshold value. In one

aspect, N equals X and N equals Y. In one aspect, the CpG site is considered to be

differentially methylated based on criteria positively correlated with Ncancer and

negatively correlated with Nnon-cancer. In one aspect, the CpG site is considered to be

differentially differentially methylated based methylated on a on based ranked score of a ranked (Ncancer score + 1)/(Ncancer of (Ncancer Nnon- + 1)/(Ncancer + N

cancer cancer ++ 2). 2).

[0011] In one aspect, in each of the different oligonucleotide-containing probes,

the sequence of at least 30 bases in length can be complementary to either (1) a

sequence within a genomic region selected from the genomic regions set forth in

any one of Lists 1-8; or (2) a sequence that varies from the sequence of (1) only by one or more transitions, wherein each respective transition of the one or more transitions occurs at a cytosine in the genomic region. In one aspect, the plurality of different oligonucleotide-containing probes can be each conjugated to an affinity moiety. The affinity moiety can be biotin. In one aspect, for at least one of the different oligonucleotide-containing probes, the sequence of at least 30 bases can be complementary to the sequence that varies from the sequence of (1) only by one or more transitions, wherein each respective transition of the one or more transitions occurs at a cytosine in the genomic region. In another aspect, for at least

500, 1,000, 2,000, 2,500, 5,000, 6,000, 10,000, 15,000, 20,000, 25,000, or 50,000

of each of the different oligonucleotide-containing probes, the sequence of at least

30 bases can be complementary to the sequence that varies from the sequence of

(1) only by one or more transitions, wherein each respective transition of the one or

more transitions occurs at a cytosine in the genomic region. In one aspect, at least

80%, 90%, or 95% of the oligonucleotide-containing probes in the bait set do not

include an at least 30, at least 40, or at least 45 base sequence that has 20 or more

off-target regions of the genome. In another aspect, the oligonucleotide-containing

probes in the bait set do not include an at least 30, at least 40, or at least 45 base

sequence that has 20 or more off-targets regions of the genome. The sequence of at

least 30 bases can be at least 40 bases, at least 45 bases, at least 50 bases, at least

60 bases, at least 75, or at least 100 bases in length. Each of the oligonucleotide-

containing probes can have a nucleic acid sequence of at least 45, 40, 75, 100, or

120 bases in length. Each of the oligonucleotide-containing probes can have a

nucleic acid sequence of no more than 300, 250, 200, or 150 bases in length.

[0012] In one aspect, the different oligonucleotide-containing probes comprise at

least 500, at least 1000, at least 2,000, at least 2,500, at least 5,000, at least 6,000,

at least 7,500, and least 10,000, at least 15,000, at least 20,000, or at least 25,000

different pairs of probes, wherein each pair of probes comprises a first probe and

second probe, wherein the second probe differs from the first probe and overlaps

with the first probe by an overlapping sequence that is at least 30, at least 40, at

least 50, or at least 60 nucleotides in length. In one aspect, the bait set can include

oligonucleotide-containing probes that are configured to target at least 20%, at

least 25%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at

least least 80%, 80%,atatleast 90%, least at least 90%, 95%, 95%, at least or 100% or of the of 100% genomic regions identified the genomic in regions identified in

PCT/US2019/053509

any one of Lists 1-8. In another aspect, the bait set can include oligonucleotide-

containing probes that are configured to target at least 20%, at least 25%, at least

30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least

90%, at least 95%, or 100% of the genomic regions identified in List 1. The bait set

can include oligonucleotide-containing probes that are configured to target at least

20%, at least 25%, at least 30%, at least 40%, at least 50%, at least 60%, at least

70%, at least 80%, at least 90%, at least 95%, or 100% of the genomic regions

identified in List 3. An entirety of oligonucleotide probes in the bait set can be

configured to hybridize to fragments obtained from cfDNA molecules

corresponding to at least 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the

genomic regions in a list selected from any one of Lists 1-8. An entirety of

oligonucleotide-containing probes in the bait set can be configured to hybridize to

fragments obtained from cfDNA molecules corresponding to at least 500, 1,000,

5000, 10,000, 15,000, 20,000, at least 25,000, or at least 30,000 genomic regions in

any one of Lists 1-8. An entirety of oligonucleotide-containing probes in the bait

set can be configured to hybridize to fragments obtained from cfDNA molecules

corresponding to at least 50, 60, 70, 80, 90, 100, 120, 150, or 200 genomic regions

in any one of Lists 1-8.

[0013] The plurality of oligonucleotide-containing probes can comprise at least

500, 1,000, 5,000, or 10,000 different subsets of probes, wherein each subset of

probes comprises a plurality of probes that collectively extend across a genomic

region selected from the genomic regions of any one of Lists 1-8 in a 2x tiled

fashion. The plurality of probes that collectively extend across the genomic region

in a 2x tiled fashion can comprise at least one pair of probes that overlap by a

sequence of at least 30 bases, at least 40 bases, at least 50 bases, or at least 60 bases

in length. The plurality of probes can collectively extend across portions of the

genome that collectively can be a combined size of between 0.2 and 15 MB,

between 0.5 MB and 15 MB, between 1 MB and 15 MB, between 3 MB and 12

MB, between 3 MB and 7, MB, between 5 MB and 9 MB, or between 7 MB and

12 MB. A subset of the different oligonucleotide-containing probes in the bait set

can be designed to hybridize to cfDNA fragments derived from one or more

genomics region from either List 4 or List 6. A subset of the different

oligonucleotide-containing probes in the bait set can be designed to target at least

2, at least 10, at least 50, at least 100, at least 1000, or at least 5000, at least 8000,

at least 10,000 or at least 20,000 of the genomic regions from either List 4 or List

6. The different oligonucleotide-containing probes can each comprise less than 20,

15, 10, 8, or 6 CpG detection sites. In one aspect, at least 80%, 85%, 90%, 92%,

95%, or 98% of the plurality of oligonucleotide-containing probes in the bait set

can have exclusively either CpG or CpA on all CpG detection sites. The

oligonucleotide-containing probes of the bait set can correspond with a number of

genomic regions selected from the genomic regions of any one of Lists 1-8,

wherein at least 30% of the genomic regions that correspond with the probes in the

bait set are in exons or introns. The oligonucleotide-containing probes of the bait

set can correspond with a number of genomic regions, wherein at least 15% or at

least 20% of the genomic regions that correspond with probes in the bait set are in

exons. The oligonucleotide-containing probes of the bait set can correspond with a

number of genomic regions, wherein less than 10% of the genomic regions that

correspond with probes in the bait set are intergenic regions. In one aspect, for each

of the different oligonucleotide-containing probes, the at least 30 nucleotide

sequence can be complementary to a sequence that varies from the sequence of the

genomic region by one or more transitions at all CpG sites within the sequence sequence.In In

another aspect, for oligonucleotide-containing probes that vary with respect to the

sequence within the genomic region by one or more transitions, a transition can

occur at each CpG site within the genomic region. The different oligonucleotide-

containing probes can be complementary to cfDNA fragments that have been

converted to replace cytosine with uracil, wherein the cfDNA fragments are found

at least 2-fold, 10-fold, 20-fold, 50-fold, 100-fold, or 1000-fold more frequently in

cfDNA from cancer subjects than from cfDNA from non-cancer subjects.

[0014] In one embodiment, provided herein is a mixture comprising a bait set and

converted cfDNA. The converted cfDNA can comprise bisulfite-converted cfDNA.

The converted cfDNA can comprise cfDNA that has been converted via cytosine

deaminase. In one aspect, provided herein is a method for enriching a converted

cfDNA sample comprising contacting the cell-free DNA sample with the bait set

and enriching the sample for a first set of genomic regions by hybridization

capture.

[0015] In another aspect, provided herein is a method for providing sequence

information informative of a presence or absence of a cancer, a stage of cancer, or

a type of cancer, the method comprising: processing cell-free DNA from a

biological sample with a deaminating agent to generate a cell-free DNA sample

comprising deaminated nucleotides; and enriching the cell-free DNA sample for

informative cell-free DNA molecules, wherein enriching the cell-free DNA sample

for informative cell-free DNA molecules comprises contacting the cell-free DNA

with a plurality of probes that are configured to hybridize to cell-free DNA

molecules that correspond to regions identified in any one of Lists 1-8; and

sequencing the enriched cell-free DNA molecules, thereby obtaining a set of

sequence reads informative of a presence or absence of a cancer, a stage of cancer,

or a type of cancer. The plurality of probes can comprise a plurality of primers, and

enriching the cell-free DNA comprises amplifying, via PCR, the cell-free DNA

fragments using the primers. In one aspect, enriching the cell-free DNA does not

involve hybridization capture. In one aspect, the plurality of probes can be

configured to hybridize to converted fragments obtained from the cfDNA

molecules corresponding to or derived from at least 30%, 40%, 50%, 60%, 70%,

80%, 90% or 95% of the genomic regions in any one of Lists 1-8. The plurality of

probes can comprise a plurality of oligonucleotide-containing probes.

[0016] In another aspect, the method further comprises determining a cancer

classification by evaluating the set of sequence reads, wherein the cancer

classification is (a) a presence or absence of cancer;

(b) a stage of cancer; or (c) a presence or absence of a type of cancer. The cancer

classification can be a presence or absence of cancer. The cancer classification can

be a stage of cancer. The stage of cancer can be selected from Stage I, Stage II,

Stage III, and Stage IV. The cancer classification can be a presence or absence of a

type of cancer. In one aspect, the step of determining a cancer classification can

further comprise: (a) generating a test feature vector based on the set of sequence

reads; and (b) applying the test feature vector to a classifier. In one aspect, the

classifier further comprises a model trained by a training process with a cancer set

of fragments from one or more training subjects with cancer and a non-cancer set

of fragments from one or more training subjects without cancer, wherein both

cancer set of fragments and the non-cancer set of fragments comprise a plurality of

PCT/US2019/053509

training fragments. The classifier can have an area under a receiver operating

characteristic curve of greater than 0.70, greater than 0.75, greater than 0.77,

greater than 0.80, greater than 0.81, greater than 0.82, or greater than 0.83. At 99%

specificity, the classifier can have a sensitivity of at least 35%, at least 40%, at

least 45% or at least 50%. In one aspect, the step of determining a cancer

classification further comprises: (a) generating a test feature vector based on the set

of sequence reads; and (b) applying the test feature vector to a classifier. In one

aspect, the classifier comprises a model trained by a training process with a cancer

set of fragments from one or more training subjects with cancer and a non-cancer

set of fragments from one or more training subjects without cancer, wherein both

training fragments.

[0017] In one aspect, the type of cancer can be selected from the group consisting

of head and neck cancer, liver/bile duct cancer, upper GI cancer,

pancreatic/gallbladder cancer; colorectal cancer, ovarian cancer, lung cancer,

multiple myeloma, lymphoid neoplasms, melanoma, sarcoma, breast cancer, and

uterine cancer. In one aspect, the classifier, at 99.4% specificity, has a sensitivity

of at least 70%, at least 80%, at least 85%, or at least 87% in classifying head and

neck cancer. The classifier, at 99.4% specificity can have a sensitivity of at least

60%, at least 65%, at least 70%, or at least 73% in classifying liver/bile duct

cancer. The classifier, at 99.4% specificity, can have a sensitivity of at least 70%,

at least 75%, at least 80%, or at least 85% in classifying upper GI tract cancer. The

classifier, at 99.4% specificity, can have a sensitivity of at least 70%, at least 80%,

at least 85%, or at least 90% in classifying pancreatic/gall bladder cancer. The

classifier, at 99.4% specificity can have a sensitivity of at least 70%, at least 80%,

at least 90%, at least 95%, or at least 98% in classifying colorectal cancer. The

at least 85%, or at least 87% in classifying ovarian cancer. The classifier, at 99.4%

specificity, can have a sensitivity of at least 70%, at least 80%, at least 90%, at

least 95%, or at least 97% in classifying lung cancer. The classifier, at 99.4%

specificity, can have a sensitivity of at least 70%, at least 80%, at least 85%, or at

least 90% or at least 93% in classifying multiple myeloma. The classifier, at 99.4%

specificity, can have a sensitivity of at least 70%, at least 80%, at least 90%, or at least 95% or at least 98% in classifying lymphoid neoplasm. The classifier, at

99.4% specificity, can have a sensitivity of at least 70%, at least 80%, at least 90%,

or at least 95% or at least 98% in classifying melanoma. The classifier, at 99.4%

specificity, can have a sensitivity of at least 35%, at least 40%, at least 45%, or at

least 50% in classifying sarcoma. The classifier, at 99.4% specificity, can have a

sensitivity of at least 70%, at least 80%, at least 90%, or at least 95% or at least

98% in classifying breast cancer. The classifier, at 99.4% specificity, can have a

97% in classifying uterine cancer.

[0018] In another aspect, provided herein a method for determining a cancer

classification comprising the steps of a) generating a test feature vector based on

the set of sequence reads; and b) applying the test feature vector to a model

obtained by a training process with a cancer set of fragments from one or more

training subjects with a cancer and a non-cancer set of fragments from one or more

training subjects without cancer, wherein both the cancer set of fragments and the

non-cancer set of fragments comprise a plurality of training fragments. The

training process can comprise: (a) obtaining sequence information of training

fragments from a plurality of training subjects; (b) for each training fragment,

determining whether that training fragment is hypomethylated or hypermethylated,

wherein each of the hypomethylated and hypermethylated training fragments

comprises at least a threshold number of CpG sites with at least a threshold

percentage of the CpG sites being unmethylated or methylated, respectively, (c) for

each training subject, generating a training feature vector based on the

hypomethylated training fragments and hypermethylated training fragments, and

(d) training the model with the training feature vectors from the one or more

training subjects without cancer and the training feature vectors from the one or

more training subjects with cancer. In another aspect, the training process

comprises: (a) obtaining sequence information of training fragments from a

plurality plurality of of training training subjects; subjects; (b) (b) for for each each training training fragment, fragment, determining determining whether whether

that training fragment is hypomethylated or hypermethylated, wherein each of the

hypomethylated and hypermethylated training fragments comprises at least a

threshold number of CpG sites with at least a threshold percentage of the CpG sites

being unmethylated or methylated, respectively, (c) for each of a plurality of CpG sites in a reference genome: quantifying a count of hypomethylated training fragments which overlap the CpG site and a count of hypermethylated training fragments which overlap the CpG site; and generating a hypomethylation score and a hypermethylation score based on the count of hypomethylated training fragments and hypermethylated training fragments; (d) for each training fragment, generating an aggregate hypomethylation score based on the hypomethylation score of the

CpG sites in the training fragment and an aggregate hypermethylation score based

on the hypermethylation score of the CpG sites in the training fragment; (e) for

each training subject: ranking the plurality of training fragments based on

aggregate hypomethylation score and ranking the plurality of training fragments

based on aggregate hypermethylation score; and generating a feature vector based

on the ranking of the training fragments; (f) obtaining training feature vectors for

one or more training subjects without cancer and training feature vectors for the

one or more training subjects with cancer; and (g)training the model with the

feature vectors for the one or more training subjects without cancer and the feature

vectors for the one or more training subjects with cancer. The model can comprise

one of a kernel logistic regression classifier, a random forest classifier, a mixture

model, a convolutional neural network, and an autoencoder model. In another

aspect, the method further comprises the steps of: obtaining a cancer probability for

the test sample based on the model; and comparing the cancer probability to a

threshold probability to determine whether the test sample is from a subject with

cancer or without cancer. The method can further comprise administering an anti-

cancer agent to the subject.

[0019] In another aspect, provided herein is a method of treating a cancer patient,

the method comprising, identified as a cancer subject by the methods above and

administering an anti-cancer agent to a subject. The anti-cancer agent can be a

chemotherapeutic agent selected from the group consisting of alkylating agents,

antimetabolites, anthracyclines, anti-tumor antibiotics, cytoskeletal disruptors

(taxans), topoisomerase inhibitors, mitotic inhibitors, corticosteroids, kinase

inhibitors, nucleotide analogs, and platinum-based agents.

[0020] Provided herein is a method for assessing whether a subject has a cancer,

the method comprising:

[0021] obtaining cfDNA from the subject; isolating a portion of the cfDNA from

the subject by hybridization capture; obtaining sequence reads derived from the

captured cfDNA to determine methylation states cfDNA fragments; applying a a

classifier to the sequence reads; and

[0022] determining whether the subject has cancer based on application of the

classifier; wherein the classifier has an area under the receiver operator

greater than 0.80, greater than 0.81, greater than 0.82, or greater than 0.83. In one

aspect, the method further comprises converting unmethylated cytosines in the

cfDNA to uracil prior to isolating the portion of the cfDNA from the subject by

hybridization capture. In another aspect, the method further comprises converting

unmethylated cytosines in the cfDNA to uracil after isolating the portion of the

cfDNA from the subject by hybridization capture. The classifier can be a binary

classifier. In one aspect, isolating a portion of the cfDNA from the subject by

hybridization capture comprises contacting the cell-free DNA with a bait set

comprising a plurality of different oligonucleotide-containing probes.

[0023] In another aspect, provided herein is a method for identifying genomic

regions that exhibit differential methylation in cancer samples relative to non-

cancer samples, the method comprising: (a) obtaining sequence reads of converted

cfDNA from both cancer subjects and non-cancer subjects; (b) identifying, based

on the sequence reads, cfDNA fragments that:(i) have a p-value rarity in non-

cancerous samples of below a threshold value; and (ii) have at least X CpG sites,

wherein at least Y% of the CpG sites are methylated, wherein X is at least 4, 5, 6,

7, 8, 9, or 10 and Y is at least 70; and (c) for each of a plurality of CpG sites in a

reference genome, counting both (1) a number of cancer subjects (Ncancer) and (2) a

number of non-cancer subjects (Nnoncancer) that have a fragment identified in step

(b); (d) for each of the plurality of CpG sites in the reference genome, determining

whether the CpG site is differentially methylated in cancer samples based on

criteria comprising Ncancer and Nnon-cancer; (e) identifying a genomic region as

differentially methylated in cancer based, at least in part, on inclusion of a

differentially methylated CpG site within the genomic region. In another aspect,

provided herein is a method for identifying genomic regions that exhibit

11 differential methylation in cancer samples relative to non-cancer samples, the method comprising:

[0024] (a) obtaining sequence reads of converted cfDNA from both cancer subjects

and non-cancer subjects; (b) identifying, based on the sequence reads, cfDNA

fragments that:(i) have at least X CpG sites, wherein at least Y% of the CpG sites

are unmethylated, wherein X is 4, 5, 6, 7, 8, 9, or 10 and Y is at least 70; and (ii)

have a p-value rarity in non-cancerous samples of below a threshold value;

[0025] (c) for each of a plurality of CpG sites in a reference genome, counting both

(1) a number of cancer subjects (Ncancer) and (2) a number of non-cancer subjects

(Nnoncancer) that have a fragment identified in step (b); (d) for each of the plurality of

CpG sites in the reference genome, determining whether the CpG site is

differentially methylated in cancer samples based on criteria comprising Ncancer and

Nnon-cancer; (e) identifying a genomic region as differentially methylated in cancer

based, at least in part, on inclusion of a differentially methylated CpG site within

the genomic region. In one aspect, the CpG site is considered to be differentially

methylated based on criteria positively correlated with Ncancer and negatively

correlated with Nnon-cancer. In one aspect, the CpG site is considered to be

differentially methylated when (Ncancer 1)/(Ncancer + Nnon-cancer + 1)/(Ncancer + 2) + Nnon-cancer isis + 2) greater than greater than a a threshold value. Each of the identified genomic regions can have at least X CpG

sites, wherein X is 4, 5, or 6. In one aspect, at least 10%, at least 20%, at least 30%,

at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90%

of the identified regions are from any one of Lists 1-8.

[0026] In one aspect, provided herein is a method for developing a bait set for

hybridization capture of cfDNA from genomic regions that are differentially

methylated between cancer and non-cancer, the method comprising: identifying at

least 1000, at least 5,000, at least 10,000, at least 25,000, or at least 30,000

differentially methylated genomic regions of the genome by comparison of one or

more parameters derived from cfDNA fragments from cancer subject to one or

more parameters derived from cfDNA fragments from non-cancer subjects; and

designing, in silico, a plurality of oligonucleotide-containing probes that include a

sequence of at least 30 bases in length that is complementary to either (1) a

sequence of a genomic region or (2) a sequence that differs from the sequence of

the genomic region only by one or more transitions, wherein each respective transition occurs at a cytosine in the genomic region. The method can further comprise removing, in silico, probes that have at least X off-target regions, wherein

X is at least one. X can be at least 5, at least 10, or at least 20. The method can

further comprise synthesizing the oligonucleotide-containing probes that were

designed in silico.

[0027] In one aspect, provided herein is a method for selecting probes for

hybridization capture of cfDNA, the method comprising: identifying a first set of

genomic regions that are preferentially hypermethylated in cfDNA from cancer

subjects relative to non-cancer subjects; identifying a second set of genomic

regions that are preferentially hypomethylated in cfDNA from cancer subjects

relative to non-cancer subjects; and selecting probes for hybridization capture of

cfDNA corresponding to the first set of genomic regions and the second set of

genomic regions, wherein the probes comprise a first set of probes for

hybridization capture of cfDNA corresponding to the first set of genomic regions

and a second set of probes for hybridization capture of cfDNA corresponding to the

second set of genomic regions; wherein the probes comprise at least 500, at least

1,000, at least 2,500, at least 5,000, at least 10,000, at least 20,000 subsets of

probes, wherein each subset of probes comprises a plurality of probes that extend

across a genomic region in a 2x tiled fashion. The second set of probes for

hybridization capture can comprise selecting probes that differ from a sequence in

the genomic region only by one or more transitions, wherein each transition occurs

at a nucleotide corresponding to a cytosine in the genomic region. In one aspect,

selecting probes for hybridization capture can comprise filtering out probes that

have more than a threshold number of off-target regions. Each subset of probes can

comprise at least three probes. Each probe can be between 75 and 200, between

100 and 150, between 110 and 130, or 120 nucleotides in length.

[0028] In another aspect, provided herein is an assay panel for enriching cfDNA

molecules for cancer diagnosis, the assay panel comprising: at least 500 different

pairs of polynucleotide probes, wherein each pair of the at least 500 pairs of probes

(i) comprises two different probes configured to overlap with each other by an

overlapping sequence of 30 or more nucleotides and (ii) is configured to hybridize

to a modified fragment obtained from processing of the cfDNA molecules, wherein

each of the cfDNA molecules corresponds to or is derived from one or more

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

genomic regions, wherein each of the one or more genomic regions comprises at

least five methylation sites and has an anomalous methylation pattern in cancerous

training samples relative to non-cancerous training samples. The overlapping

sequence can comprise at least 40, 50, 75, or 100 nucleotides. The assay panel can

comprise at least 1,000, 2,000, 2,500, 5,000, 6,000, 7,500, 10,000, 15,000, 20,000,

or 25,000 pairs of probes.

[0029] In another aspect, provided herein is an assay panel for enriching cfDNA

molecules for cancer diagnosis, the assay panel comprising: at least 1,000

polynucleotide probes, wherein each of the at least 1,000 probes is configured to

hybridize to a modified polynucleotide obtained from processing of the cfDNA

molecules, wherein each of the cfDNA molecules corresponds to or is derived

from, one or more genomic regions, wherein each of the one or more genomic

regions comprises at least five methylation sites, and has an anomalous

methylation pattern in cancerous training samples relative to non-cancerous

samples. The processing of the cfDNA molecules can comprise converting

unmethylated C (cytosine) to U (uracil) in the cfDNA molecules. Each of the

polynucleotide probes on the panel can be conjugated to an affinity moiety. The

affinity moiety can be a biotin moiety. Each of the one or more genomic regions

can be either hypermethylated or hypomethylated in the cancerous training samples

relative to non-cancerous reference samples. In one aspect, at least 80%, 85%,

90%, 92%, 95%, or 98% of the probes on the panel can have exclusively either

CpG or CpA on CpG detection sites. Each of the probes on the panel can comprise

less than 20, 15, 10, 8, or 6 CpG detection sites. Each of the probes on the panel

can be designed to have fewer than 20, 15, 10, or 8 off-target genomic regions. In

one aspect, the fewer than 20 off-target genomic regions can be identified using a

k-mer seeding strategy. In another aspect, the fewer than 20 off-target genomic

regions can be identified using k-mer seeding strategy combined to local alignment

at seed locations.

[0030] The assay panel can comprise at least 1,000, 2,000, 2,500, 5,000, 10,000,

12,000, 15,000, 20,000, or 25,000 probes. In one aspect, at least 500 pairs of

probes or the at least 1,000 probes together can comprise at least 0.2 million, 0.4

million, 0.6 million, 0.8 million, 1 million, 2 million, 4 million, or 6 million

nucleotides. Each of the probes on the panel can comprise at least 50, 75, 100, or

120 nucleotides. Each of the probes on the panel can comprise less than 300, 250,

200, or 150 nucleotides. Each of the probes on the panel can comprise 100-150

nucleotides. In one aspect, at least 30% of the genomic regions are in exons or

introns. In another aspect, at least 15% of the genomic regions can be in exons. In

another aspect, at least 20% of the genomic regions can be in exons. Each of the

one or more genomic regions can be selected from one of Lists 1-8. An entirety of

probes on the assay panel together can be configured to hybridize to modified

fragments obtained from the cfDNA molecules corresponding to or derived from at

least 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in one

or or more moreofofLists 1-8. Lists 1-8An An entirety of probes entirety on theonassay of probes the panel assay together are panel together are

configured to hybridize to modified fragments obtained from the cfDNA molecules

corresponding to or derived from at least 500, 1,000, 5000, 10,000 or 15,000

genomic regions in one or more of Lists 1-8. The processing of the cfDNA

molecules can comprise converting unmethylated C (cytosine) to U (uracil) in the

cfDNA molecules. Each of probes on the panel can be conjugated to an affinity

moiety. The affinity moiety can be biotin. At least 80%, 85%, 90%, 92%, 95%, or

98% of the probes on the panel can have exclusively either CpG or CpA on CpG

detection sites.

[0031] In another aspect, provided herein is a method of providing sequence

information informative of a presence or absence of cancer, the method comprising

the steps of: (a) obtaining a test sample comprising a plurality of cfDNA test

molecules; (b) processing the cfDNA test molecules, thereby obtaining

converted test fragments; (c) contacting the converted test fragments with an assay

panel, thereby enriching a subset of the converted test fragments by hybridization

capture; and (d) sequencing the subset of the converted test fragments, thereby

obtaining a set of sequence reads. The converted test fragments can be bisulfite-

converted test fragments. In another aspect, the method further comprises

determining a cancer classification by evaluating the set of sequence reads,

wherein the cancer classification is (a) a presence or absence of cancer; (b) a stage of cancer; (c) a presence or absence of a type of cancer. In one aspect,

the step of determining a cancer classification can comprise: (a) generating a test

feature vector based on the set of sequence reads; and (b) applying the test feature

vector to a classifier. The classifier can comprise a model trained by a training

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

process with a cancer set of fragments from one or more training subjects with

cancer and a non-cancer set of fragments from one or more training subjects

without cancer, wherein both cancer set of fragments and the non-cancer set of

fragments comprise a plurality of training fragments. The classifier can have an

area under a receiver operating characteristic curve of greater than 0.70, greater

than 0.75, greater than 0.77, greater than 0.80, greater than 0.81, greater than 0.82,

or greater than 0.83. At 99% specificity, the classifier can have a sensitivity of at

least 35%, at least 40%, at least 45% or at least 50%. In another aspect, the step of

determining a cancer classification further comprises: (a) generating a test feature

vector based on the set of sequence reads; and (b) applying the test feature vector

to a classifier. The classifier can comprise a model trained by a training process

with a cancer set of fragments from one or more training subjects with cancer and a

non-cancer set of fragments from one or more training subjects without cancer,

wherein both cancer set of fragments and the non-cancer set of fragments comprise

a plurality of training fragments.

[0032] The type of cancer can be selected from the group consisting of head and

neck cancer, liver/bile duct cancer, upper GI cancer, pancreatic/gallbladder cancer;

colorectal cancer, ovarian cancer, lung cancer, multiple myeloma, lymphoid

neoplasms, melanoma, sarcoma, breast cancer, and uterine cancer. In one aspect,

the classifier, at 99.4% specificity, has a sensitivity of at least 70%, at least 80%, at

least 85%, or at least 87% for classifying head and neck cancer. The classifier, at

99.4% specificity can have a sensitivity of at least 60%, at least 65%, at least 70%,

or at least 73% for classifying liver/bile duct cancer. The classifier, at 99.4%

specificity, can have a sensitivity of at least 70%, at least 75%, at least 80%, or at

least 85% for classifying upper GI tract cancer. The classifier, at 99.4% specificity,

can have a sensitivity of at least 70%, at least 80%, at least 85%, or at least 90% for

classifying pancreatic/gall bladder cancer. The classifier, at 99.4% specificity can

have a sensitivity of at least 70%, at least 80%, at least 90%, at least 95%, or at

least 98% in classifying colorectal cancer. The classifier, at 99.4% specificity, can

have a sensitivity of at least 70%, at least 80%, at least 85%, or at least 87% in

classifying ovarian cancer. The classifier, at 99.4% specificity, can have a

sensitivity of at least 70%, at least 80%, at least 90%, at least 95%, or at least 97%

in classifying lung cancer. The classifier, at 99.4% specificity, can have a sensitivity of at least 70%, at least 80%, at least 85%, or at least 90% or at least

93% in classifying multiple myeloma. The classifier, at 99.4% specificity, can have

a sensitivity of at least 70%, at least 80%, at least 90%, or at least 95% or at least

98% in classifying lymphoid neoplasm. The classifier, at 99.4% specificity, can

have a sensitivity of at least 70%, at least 80%, at least 90%, or at least 95% or at

least 98% in classifying melanoma. The classifier, at 99.4% specificity, can have a

sensitivity of at least 35%, at least 40%, at least 45%, or at least 50% in classifying

sarcoma. The classifier, at 99.4% specificity, can have a sensitivity of at least 70%,

at least 80%, at least 90%, or at least 95% or at least 98% in classifying breast

cancer. The classifier, at 99.4% specificity,, can have a sensitivity of at least 70%,

at least 80%, at least 90%, or at least 95% or at least 97% in classifying uterine

cancer. cancer.

[0033] In one aspect, the method of cancer classification further comprises the

steps of: (a) generating a test feature vector based on the set of sequence reads;

and (b) applying the test feature vector to a model obtained by a training process

with a cancer set of fragments from one or more training subjects with a cancer and

a non-cancer set of fragments from one or more training subjects without cancer,

wherein both the cancer set of fragments and the non-cancer set of fragments

comprise a plurality of training fragments. The training process can comprise: (a)

obtaining sequence information of training fragments from a plurality of training

subjects; (b) for each training fragment, determining whether that training fragment

is hypomethylated or hypermethylated, wherein each of the hypomethylated and

hypermethylated training fragments comprises at least a threshold number of CpG

sites with at least a threshold percentage of the CpG sites being unmethylated or

methylated, respectively, (c) for each training subject, generating a training feature

vector based on the hypomethylated training fragments and a training feature

vector based on the hypermethylated training fragments, and (d) training the model

with the training feature vectors from the one or more training subjects without

cancer and the training feature vectors from the one or more training subjects with

cancer. The training process can further comprise: (a) obtaining sequence

information of training fragments from a plurality of training subjects; (b) for each

training fragment, determining whether that training fragment is hypomethylated or

hypermethylated, wherein each of the hypomethylated and hypermethylated

17 training fragments comprises at least a threshold number of CpG sites with at least a threshold percentage of the CpG sites being unmethylated or methylated, respectively, (c) for each of a plurality of CpG sites in a reference genome: quantifying a count of hypomethylated training fragments which overlap the CpG site and a count of hypermethylated training fragments which overlap the CpG site; and generating a hypomethylation score and a hypermethylation score based on the count of hypomethylated training fragments and hypermethylated training fragments; (d) for each training fragment, generating an aggregate hypomethylation score based on the hypomethylation score of the CpG sites in the training fragment and an aggregate hypermethylation score based on the hypermethylation score of the CpG sites in the training fragment; (e) for each training subject: ranking the plurality of training fragments based on aggregate hypomethylation score and ranking the plurality of training fragments based on aggregate hypermethylation score; and generating a feature vector based on the ranking of the training fragments; (f) obtaining training feature vectors for one or more training subjects without cancer and training feature vectors for the one or more training subjects with cancer; and (g) training the model with the feature vectors for the one or more training subjects without cancer and the feature vectors for the one or more training subjects with cancer. The model can comprise one of a kernel logistic regression classifier, a random forest classifier, a mixture model, a convolutional neural network, and an autoencoder model. In one aspect, the method of cancer classification can further comprise the steps of: (a) obtaining a cancer probability for the test sample based on the model; and (b) comparing the cancer probability to a threshold probability to determine whether the test sample is from a subject with cancer or without cancer. In one aspect, the method of identifying a cancer subject can further comprise administering an anti-cancer agent to the subject. The anti-cancer agent can be a chemotherapeutic agent selected from the group consisting of alkylating agents, antimetabolites, anthracyclines, anti-tumor antibiotics, cytoskeletal disruptors (taxans), topoisomerase inhibitors, mitotic inhibitors, corticosteroids, kinase inhibitors, nucleotide analogs, and platinum-based agents.

[0034] In another aspect, provided herein is a method comprising the steps of: (a)

obtaining a set of sequence reads of modified test fragments, wherein the modified test fragments are or have been obtained by processing a set of nucleic acid fragments from a test subject, wherein each of the nucleic acid fragments corresponds to or is derived from a plurality of genomic regions selected from any one of Lists 1-8; and (b) applying the set of sequence reads or a test feature vector obtained based on the set of sequence reads to a model obtained by a training process with a cancer set of fragments from one or more training subjects with cancer and a non-cancer set of fragments from one or more training subjects without cancer, wherein both cancer set of fragments and the non-cancer set of fragments comprise a plurality of training fragments. The method can further comprise the step of obtaining the test feature vector comprising: (a) for each of the nucleic acid fragments, determining whether the nucleic acid fragment is hypomethylated or hypermethylated, wherein each of the hypomethylated and hypermethylated nucleic acid fragments comprises at least a threshold number of

CpG sites with at least a threshold percentage of the CpG sites being unmethylated

or methylated, respectively; (b) for each of a plurality of CpG sites in a reference

genome: quantifying a count of hypomethylated nucleic acid fragments which

overlap the CpG site and a count of hypermethylated nucleic acid fragments which

overlap the CpG site; and

[0035] generating a hypomethylation score and a hypermethylation score based on

the count of hypomethylated nucleic acid fragments and hypermethylated nucleic

acid fragments; (c) for each nucleic acid fragment, generating an aggregate

hypomethylation score based on the hypomethylation score of the CpG sites in the

nucleic acid fragment and an aggregate hypermethylation score based on the

hypermethylation score of the CpG sites in the nucleic acid fragment; (d) ranking

the plurality of nucleic acid fragments based on aggregate hypomethylation score

and ranking the plurality of nucleic fragments based on aggregate

hypermethylation score; and (e) generating the test feature vector based on the

ranking of the nucleic acid fragments. The training process can comprise: (a) for

each training fragment, determining whether that training fragment is

hypomethylated or hypermethylated, wherein each of the hypomethylated and

hypermethylated training fragments comprises at least a threshold number of CpG

methylated, respectively, (b) for each training subject, generating a training feature vector based on the hypomethylated training fragments and a training feature vector based on the hypermethylated training fragments, and (c) training the model with the training feature vectors from the one or more training subjects without cancer and the feature vectors from the one or more training subjects with cancer.

The training process can comprise: (a) for each training fragment, determining

whether that training fragment is hypomethylated or hypermethylated, wherein

each of the hypomethylated and hypermethylated training fragments comprises at

least a threshold number of CpG sites with at least a threshold percentage of the

CpG sites being unmethylated or methylated, respectively, (b) for each of a

plurality of CpG sites in a reference genome: quantifying a count of

hypomethylated training fragments which overlap the CpG site and a count of

hypermethylated training fragments which overlap the CpG site; andgenerating a

hypomethylation score and a hypermethylation score based on the count of

hypomethylated training fragments and hypermethylated training fragments; (c) for

each training fragment, generating an aggregate hypomethylation score based on

the hypomethylation score of the CpG sites in the training fragment and an

aggregate hypermethylation score based on the hypermethylation score of the CpG

sites in the training fragment; (d) for each training subject:

[0036] ranking the plurality of training fragments based on aggregate

hypomethylation score and ranking the plurality of training fragments based on

aggregate hypermethylation score; and

[0037] generating a feature vector based on the ranking of the training fragments;

(e) obtaining training feature vectors for one or more training subjects without

cancer and training feature vectors for the one or more training subjects with

cancer; and (f) training the model with the feature vectors for the one or more

training subjects without cancer and the feature vectors for the one or more training

subjects with cancer.

[0038] In one aspect, the method can further comprise quantifying a count of

hypomethylated training fragments which overlap that CpG site and a count of

hypermethylated training fragments which overlap that CpG site for each CpG site

in a reference genome comprising the steps of : (a) quantifying a cancer count of

hypomethylated training fragments from the one or more training subjects with

cancer that overlap that CpG site and a non-cancer count of hypomethylated training fragments from the one or more training subjects without cancer that overlap that CpG site; and (b) quantifying a cancer count of hypermethylated training fragments from the one or more training subjects with cancer that overlap that CpG site and a non-cancer count of hypermethylated training fragments from the one or more training subjects without cancer that overlap that CpG site. For each CpG site in a reference genome, generating a hypomethylation score and a hypermethylation score based on the count of hypomethylated training fragments and hypermethylated training fragments further comprises: (a) for generating the hypomethylation score, calculating a hypomethylation ratio of the cancer count of hypomethylated training fragments over a hypomethylation sum of the cancer count of hypomethylated training fragments and the non-cancer count of hypomethylated training fragments; and (b) for generating the hypermethylation score, calculating a hypermethylation ratio of the cancer count of hypermethylated training fragments over a hypermethylation sum of the cancer count of hypermethylated training fragments and the non-cancer count of hypermethylated training fragments. The model can comprise one of a kernel logistic regression classifier, a random forest classifier, a mixture model, a convolutional neural network, and an autoencoder model. In one aspect, the set of sequence reads can be obtained by using the assay panel described in this invention.

[0039] In one aspect, provided herein is a method of designing an assay panel for

cancer diagnosis, comprising the steps of: (a) identifying a plurality of genomic

regions, wherein each of the plurality of genomic regions (i) comprises at least 30

nucleotides, and (ii) comprises at least five methylation sites, (b) selecting a subset

of the genomic regions, wherein the selection is made when cfDNA molecules

corresponding to or derived from each of the genomic regions in cancer training

samples have an anomalous methylation pattern, wherein the anomalous

methylation pattern comprises at least five methylation sites known to be, or

identified as, either hypomethylated or hypermethylated, and (c) designing the

assay panel comprising a plurality of probes, wherein each of the probes is

configured to hybridize to a modified fragment obtained from processing cfDNA

molecules corresponding to or derived from one or more of the subset of the

genomic regions. The processing of the cfDNA molecules can comprise converting

unmethylated C (cytosine) to U (uracil) in the cfDNA molecules.

[0040] In another aspect, provided herein is a cancer assay panel, comprising: at

least 500 pairs of probes, wherein each pair of the at least 500 pairs comprises two

probes configured to overlap each other by an overlapping sequence, wherein the

overlapping sequence comprises a 30-nucleotide sequence, and wherein the 30-

nucleotide sequence is configured to have sequence complementarity with one or

more genomic regions, wherein the one or more genomic regions have at least five

methylation sites, and wherein the at least five methylation sites have an abnormal

methylation pattern in non-cancerous samples or cancerous samples. The

overlapping sequence comprises at least 40, 50, 75, or 100 nucleotides. The cancer

assay panel can comprise at least 1,000, 2,000, 2,500, 5,000, 6,000, 7,500, 10,000,

15,000, 20,000 or 25,000 pairs of probes.

[0041] Inanother

[0041] In another aspect, aspect, provided provided herein herein is a cancer is a cancer assaycomprising: assay panel, panel, comprising: at at

least 1,000 probes, wherein each of the probes is designed as a hybridization probe

complementary to one or more genomic regions, wherein each of the genomic

regions comprises: (i) at least 30 nucleotides, and (ii) at least five methylation sites,

wherein the at least five methylation sites have an abnormal methylation pattern

and are either hypomethylated or hypermethylated in cancerous samples or non-

cancerous samples. The abnormal methylation pattern can have at least a threshold

p-value rarity in the non-cancerous samples. In one aspect, each of the probes is

designed to have less than 20 off-target genomic regions. In one aspect, the less

than 20 off-target genomic regions are identified using a k-mer seeding strategy. In

another aspect, the less than 20 off-target genomic regions are identified using k-

mer seeding strategy combined to local alignment at seed locations.

[0042] In one aspect, each of the genomic regions can be selected based on criteria

comprising: (a) a number (Ncancer) of the cancerous samples including at least one

cfDNA fragment having the abnormal methylation pattern; and (b) a number

(Nnon-cancer) of the non-cancerous samples including at least one cfDNA fragment

having the abnormal methylation pattern. Each of the genomic regions can be

selected based on criteria positively correlated to Ncancer and inversely correlated

to the sum of Ncancer and Nnon-cancer Nnon-cancer.comprising comprisingat atleast least1,000, 1,000,2,000, 2,000,2,500, 2,500,5,000, 5,000,

10,000, 12,000, 15,000, 20,000, 30,000, 40,000, or 50,000 probes. In one aspect,

the at least 500 pairs of probes or the at least 1,000 probes together comprise at

least 0.2 million, 0.4 million, 0.6 million, 0.8 million, 1 million, 2 million, 3

PCT/US2019/053509

million, 4 million, 5 million, or 6 million nucleotides. Each of the probes can

comprise at least 50, 75, 100, or 120 nucleotides. Each of the probes can comprise

less than 300, 250, 200, or 150 nucleotides. Each of the probes can comprise 100-

150 nucleotides. Each of the probes can comprise less than 20, 15, 10, 8, or 6

methylation sites. In one aspect, at least 80%, 85%, 90%, 92%, 95%, or 98% of

the at least five methylation sites are either methylated or unmethylated in the

cancerous samples.

[0043] In one aspect, each of the probes is configured to have less than 20, 15, 10,

or 8 off-target genomic regions. In one aspect, at least 30% of the genomic regions

are in exons or introns. In another aspect, at least 15% of the genomic regions are

in exons. In another aspect, at least 20% of the genomic regions are in exons. In

another aspect, less than 10% of the genomic regions are in intergenic regions. The

genomic regions can be selected from any one of Lists 1-8. The genomic regions

can be selected from List 3. The genomic regions can be selected from List 8. The

genomic regions can comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%,

90% or 95% of the genomic regions in any one of Lists 1-8. The genomic regions

can comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the

genomic regions in List 3. The genomic regions can comprise at least 20%, 30%,

40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in List 5. The

genomic regions can comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%,

90% or 95% of the genomic regions in List 8. In one aspect, the at least 1,000 or at

least 2,000 probes are configured to be complementary to at least 500, 1,000, 5000,

10,000 or 15,000 genomic regions in any one of Lists 1-8. In another aspect, the at

least 1,000 or at least 2,000 probes are configured to be complementary to at least

500, 1,000, 5000, 10,000 or 15,000 genomic regions in List 3. In another aspect,

the at least 1,000 or at least 2,000 probes are configured to be complementary to at

least 500, 1,000, 5000, 10,000 or 15,000 genomic regions in List 5. In another

aspect, the at least 1,000 or at least 2,000 probes are configured to be

complementary to at least 1,000, 5000, 10,000 or 15,000 genomic regions in List 8.

The 30-nucleotide sequence can comprise at least five CpG detection sites, wherein

at least 80% of the at least five CpG detection sites comprise CpG, UpG, or CpA.

[0044] Provided herein is a cancer assay method comprising: receiving a sample

comprising a plurality of nucleic acid fragments; treating the plurality of nucleic acid fragments to convert unmethylated cytosine to uracil, thereby obtaining a plurality of converted nucleic acid fragments; hybridizing the plurality of converted nucleic acid fragments with the probes on the cancer assay panel of any of above claims; enriching a subset of the plurality of converted nucleic acid fragments; and

[0045] sequencing the enriched subset of the converted nucleic acid fragments,

thereby providing a set of sequence reads. The method can further comprise the

step of: determining a health condition by evaluating the set of sequence reads,

wherein the health condition is (i) a presence or absence of cancer, or (ii) a stage of

cancer. The set of nucleic acid fragments can be obtained from a human subject.

[0046] In another aspect, provided herein is a method for diagnosing cancer,

comprising the steps of:

[0047] (a) obtaining a set of sequence reads by sequencing a set of nucleic acid

fragments from a subject;

[0048] (b) determining methylation status of a plurality of genomic regions, the

plurality of genomic regions comprise genomic regions selected from the

genomic regions in any one of Lists 1-8; and

[0049] (c) determining a health condition of the subject by evaluating the

methylation status, wherein the health condition is (i) a presence or absence of

cancer; or (ii) a stage of cancer. The genomic regions can be selected from List 3.

The genomic regions can be selected from List 5. The genomic regions can be

selected from List 8.

[0050] In another aspect, provided herein is a method for diagnosing cancer,

comprising the steps of:

[0051] (a) obtaining a set of sequence reads by sequencing a set of nucleic acid

fragments from a subject;

[0052] (b) determining methylation status of a plurality of genomic regions, the

plurality of genomic regions comprise at least 20%, 30%, 40%, 50%, 60%, 70%,

80%, 90% or 95% of the genomic regions in any one of Lists 1-8; and (c)

determining a health condition of the subject by evaluating the methylation status,

wherein the health condition is (i) a presence or absence of cancer; or (ii) a stage of

24 cancer. The genomic regions can comprise at least 20%, 30%, 40%, 50%, 60%,

70%, 80%, 90% or 95% of the genomic regions in List 3. The genomic regions can

comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the

genomic regions in List 5. The genomic regions can comprise at least 20%, 30%,

40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in List 8.

[0053] In another aspect, provided herein is a method for diagnosing cancer,

comprising the steps of:

[0054] (a) obtaining a set of sequence reads by sequencing a set of nucleic acid

fragments from a subject;

[0055] (b) determining methylation status of a plurality of at least 1,000, 2,000,

2,500, 5,000, 6,000, 7,500, 10,000, 15,000, 20,000 or 25,000 genomic regions

among genomic regions in any one of Lists 1-8; and (c) determining a health

condition of the subject by evaluating the methylation status, wherein the health

condition is (i) a presence or absence of cancer; or (ii) a stage of cancer. In one

aspect, at least 1,000, 2,000 probes can be configured to be complementary to at

least 500, 1,000, 5000, 10,000 or 15,000 genomic regions in List 3. In one aspect,

at least 1,000, 2,000 probes can be configured to be complementary to at least 500,

1,000,5000, 1,000, 5000, 10,000 10,000 or or 15,000 15,000 genomic genomic regions regions in in List List 5. 5. In In one one aspect, aspect, at at least least

1,000, 1,000, ,2,000 2,000 probes probescan be be can configured to betocomplementary configured to at least be complementary to at500, 1,000, least 500, 1,000,

5000, 10,000 or 15,000 genomic regions in List 8.

[0056] In another aspect, provided herein is a method of designing a cancer assay

panel comprising the steps of: identifying a plurality of genomic regions, wherein

each of the plurality of genomic regions (i) comprises at least 30 nucleotides, and

(ii) comprises at least five methylation sites, wherein the at least five methylation

sites are either hypomethylated or hypermethylated, comparing methylation status

of the at least five methylation sites in each of the plurality of genomic regions

between cancerous samples and non-cancerous samples, selecting a subset of the

genomic regions, wherein at least five methylation sites of the subset of the

genomic regions have an abnormal methylation pattern in cancerous samples

relative to non-cancerous samples, and designing a cancer assay panel comprising

a plurality of probe sets, wherein each of the plurality of probe sets comprises at

least a pair of probes configured to target one of the subset of the genomic regions.

In one aspect, the abnormal methylation pattern matches that of a cfDNA fragment

from the cancerous samples overlapping at least one of the at least five methylation

sites, wherein the cfDNA has at least a threshold p-value rarity relative to a training

data set of the non-cancerous samples. In one aspect, the method further comprises

the step of selecting based on criteria comprising: (a) a number Ncancer of the

cancerous samples including cfDNA fragments having the abnormal methylation

pattern; and (b) a number Nnon-cancer of the non-cancerous samples including cfDNA

fragments having the abnormal methylation pattern. In one aspect, the step of

selecting is based on criteria positively correlated to Ncancer and inversely correlated

to the sum of Ncancer and Nnon-cancer- Nnon-cancer. Each of the plurality of probes can have less

than 20, 15, 10 or 8 off-target genomic regions. In oen aspect, the he less than 20,

15, 10, or 8 off-target genomic regions are identified using a k-mer seeding

strategy. In one aspect, the less than 20, 15, 10 or 8 off-target genomic regions are

identified using k-mer seeding strategy combined to local alignment at seed

locations. The method can further comprise the step of making the cancer assay

panel comprising the plurality of probes.

[0057] In another aspect, provided herein is a cancer assay panel comprising a

plurality of probes made by the method described above. The subset of genomic

regions in the cancer assay panel can comprise genomic regions of any one of Lists

1-8. The subset of genomic regions in the cancer assay panel can comprise

genomic regions of List 3. The subset of genomic regions in the cancer assay panel

can comprise genomic regions of List 5. The subset of genomic regions in the

cancer assay panel can comprise genomic regions of List 8. The subset of the

genomic regions in the cancer assay panel can comprise at least 20%, 30%, 40%,

50%, 60%, 70%, 80%, or 90% of the genomic regions in one or more of any one of

Lists 1-8. The subset of the genomic regions in the cancer assay panel can

comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the genomic

regions in List 3. The subset of the genomic regions in the cancer assay panel can

comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the genomic

regions in List 5. The subset of the genomic regions in the cancer assay panel can

comprise at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the genomic

regions in List 8. The subset of the genomic regions can comprise at least 500,

1,000, 5000, 10,000, 15,000, 20,000 or 25,000 genomic regions in any one of Lists

PCT/US2019/053509

1-8. The subset of the genomic regions can comprise at least 500, 1,000,5000, 1,000, 5000,

10,000, 15,000, 20,000 or 25,000 genomic regions in List 3. The subset of the

genomic regions can comprise at least 500, 1,000, 5000, 10,000, 15,000, 20,000 or

25,000 genomic regions in List 5. The subset of the genomic regions can comprise

at least 500, 1,000, 5000, 10,000, 15,000, 20,000 or 25,000 genomic regions in List

8.

[0058] In another aspect, provided herein is a cancer assay panel comprising a

plurality of probes, wherein each of the plurality of probes is configured to overlap

with one of the genomic regions in any one of Lists 1-8, and the plurality of probes

together overlap with at least 90%, 95% or 100% of the genomic regions in any

one of Lists 1-8. In another aspect, provided herein is a cancer assay panel

comprising a plurality of probes, wherein each of the plurality of probes is

configured to overlap with one of the genomic regions in any one of Lists 1-8, and

the plurality of probes together overlap with at least 500, 1,000, 5000, 10,000 or

15,000 genomic regions in any one of Lists 1-8.

[0059] The present description provides: a cancer assay panel comprising at least

500 pairs of probes, wherein each pair of the at least 500 pairs comprises two

probes configured to overlap each other by an overlapping sequence, wherein the

overlapping sequence comprises a 30-nucleotide fragment, the 30-nucleotide

fragment comprises at least five CpG sites, wherein at least 80% of the at least five

CpG sites comprise either CpG or at least 80% of the at least five CpG sites

comprise UpG, and wherein the 30-nucleotide fragment is configured to bind to

one or more genomic regions in cancerous samples, wherein the one or more

genomic regions have at least five methylation sites, wherein the at least five

methylation sites have an abnormal methylation pattern in non-cancerous samples

or cancerous samples.

[0060] In some embodiments, the overlapping sequence comprises at least 40, 50,

75, or 100 nucleotides. In some embodiments, the cancer assay panel comprises at

least 1,000, 2,000, 2,500, 5,000, 6,000, 7,500, 10,000, 15,000, 20,000 or 25,000

pairs of probes.

[0061] Another aspect of the present description provides a cancer assay panel,

comprising: at least 1,000 probes, wherein each of the probes is designed as a

27 hybridization probe complementary to one or more genomic regions, wherein each of the genomic regions comprises: (i) at least 30 nucleotides, and (ii) at least five methylation sites, wherein the at least five methylation sites have an abnormal methylation pattern and are either hypomethylated or hypermethylated in cancerous samples or non-cancerous samples.

[0062] In some embodiments, the abnormal methylation pattern has at least a a threshold p-value rarity in the non-cancerous samples. In some embodiments, each

of the probes is designed to be complementary to less than 20 off-target genomic

regions.

[0063] In some embodiments, each of the genomic regions was selected based on

criteria comprising: a number (Ncancer) of the cancerous samples including cfDNA

fragments having the abnormal methylation pattern; and a number (Nnon-cancer) of

the non-cancerous samples including cfDNA fragments having the abnormal

methylation methylationpattern. pattern.

[0064] In some embodiments, each of the genomic regions was selected based on

criteria positively correlated to Ncancer and inversely correlated to the sum of Ncancer

and Nnon-cancer. and Nnon-cancer.

[0065] In some embodiments, the cancer assay panel comprises at least 1,000,

2,000, 2,500, 5,000, 10,000, 12,000, 15,000, 20,000, 30,000, 40,000, 50,000, or

100,000 probes. In some embodiments, the at least 1,000 pairs of probes or the at

least 2,000 probes together comprise at least 0.2 million, 0.4 million, 0.6 million,

0.8 million, 1 million, 2 million, 3 million, 4 million, 5 million, 6 million

nucleotides, 7 million nucleotides, 8 million nucleotides, 9 million nucleotides, or

10 million nucleotides. In some embodiments, each of the probes comprises at least

50, 75, 100, or 120 nucleotides. In some embodiments, each of the probes

comprises less than 300, 250, 200, or 150 nucleotides. In some embodiments, each

of the probes comprises 100-150 nucleotides. In some embodiments, each of the

probes comprises less than 20, 15, 10, 8, or 6 methylation sites. In some

embodiments, at least 80%, 85%, 90%, 92%, 95%, or 98% of the at least five

methylation sites are either methylated or unmethylated in the cancerous samples.

In some embodiments, each of the probes is configured to be complementary to

less than 20, 15, 10 or 8 off-target genomic regions.

[0066] In some embodiments, at least 15%, 20%, 30%, or 40% of the genomic

regions are in exons or introns. In some embodiments, at least 5%, 10%, 15%,

20%, 30% or 40% of the genomic regions are in exons. In some embodiments, less

than 5%, 10%, 15%, 20%, 25%, or 30% of the genomic regions are in intergenic

regions. In some embodiments, between 20% and 60%, between 30% and 50%, or

between 35% and 55% of the genomic regions are in introns or exons. In some

embodiments, between 5% and 30% between 10% and 25%, or between 12% and

20% of the genomic regions are in exons. In some embodiments, between 5% and

20% of the genomic regions are in intergenic regions.

[0067] In some embodiments, the genomic regions are selected from any one of

Lists 1-8. In some embodiments, the genomic regions comprise at least 20%, 30%,

40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in any one of

Lists 1-8. In some embodiments, the at least 1,000 probes are configured to be

complementary to at least 500, 1,000, 5000, 10,000 or 15,000 genomic regions in

any one of Lists 1-8.

[0068] In another aspect, the present description provides a cancer assay method

comprising receiving a sample comprising a plurality of nucleic acid fragments;

treating the plurality of nucleic acid fragments to convert unmethylated cytosine to

uracil, thereby obtaining a plurality of converted nucleic acid fragments;

hybridizing the plurality of converted nucleic acid fragments with the probes on the

cancer assay panel of any of the above claims; enriching a subset of the converted

nucleic acid fragments; and sequencing the enriched subset of the converted

nucleic acid fragments, thereby providing a set of sequence reads.

[0069] In some embodiment, the method further comprises the step of determining

a health condition by evaluating the set of sequence reads, wherein the health

condition is a presence or absence of cancer and/or, cancer stage.

[0070] In some embodiments, the set of nucleic acid fragments is obtained from a

human subject.

[0071] In other aspect, the present description provides a method of diagnosing

cancer, comprising the steps of: (a) obtaining a set of sequence reads by

sequencing a set of nucleic acid fragments from a subject; (b) determining

methylation status of a plurality of genomic regions, the plurality of genomic regions comprise genomic regions of any one of Lists 1-8; and (c) determining a health condition of the subject by evaluating the methylation status, wherein the health condition is (i) a presence or absence of cancer; or (ii) a stage of cancer. In other aspect, the present description provides a method for diagnosing cancer, comprising the steps of: (a) obtaining a set of sequence reads by sequencing a set of nucleic acid fragments from a subject; (b) determining methylation status of a plurality of genomic regions, the plurality of genomic regions comprise at least

20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in

any one of Lists 1-8; and (c) determining a health condition of the subject by

evaluating the methylation status, wherein the health condition is (i) a presence or

absence of cancer; or (ii) a stage of cancer. In other aspects, the present description

provides a method for diagnosing cancer, comprising the steps of: (a) obtaining a

set of sequence reads by sequencing a set of nucleic acid fragments from a subject;

(b) determining methylation status of a plurality of at least 1,000, 2,000, 2,500,

5,000, 6,000, 7,500, 10,000, 15,000, 20,000 or 25,000 genomic regions among

genomic regions in any one of Lists 1-8; and (c) determining a health condition of

the subject by evaluating the methylation status, wherein the health condition is (i)

a presence or absence of cancer; or (ii) a stage of cancer.

[0072] Yet another aspect provides a method of designing a cancer assay panel

comprising the steps of: identifying a plurality of genomic regions, wherein each of

the plurality of genomic regions (i) comprises at least 30 nucleotides, and (ii)

comprises at least five methylation sites, comparing methylation status of the at

least five methylation sites in each of the plurality of genomic regions between

cancerous samples and non-cancerous samples, wherein the at least five

methylation sites are either hypomethylated or hypermethylated, selecting a subset

of the genomic regions, wherein at least five methylation sites of the subset of the

genomic regions have an abnormal methylation pattern in cancerous samples

least a pair of probes configured to target (e.g., to be complementary to either

converted or non-converted fragments corresponding to) one of the subset of the

genomic regions.

PCT/US2019/053509

[0073] In some embodiments, the abnormal methylation pattern matches that of a

cfDNA fragment from the cancerous samples overlapping at least one of the at

least five methylation sites, wherein the cfDNA has at least a threshold p-value

rarity relative to a training data set of the non-cancerous samples.

[0074] In some embodiments, the step of selecting is performed based on criteria

comprising: a number Ncancer of the cancerous samples including cfDNA fragments

having the abnormal methylation pattern; and a number Nnon-cancer of the non-

cancerous samples including cfDNA fragments having the abnormal methylation

pattern. In some embodiments, the step of selecting is based on criteria positively

correlated to Ncancer and inversely correlated to the sum of Ncancer and Nnon-cancer.

[0075] In some embodiments, each of the plurality of probes has less than 20, 15,

10 or 8 off-target genomic regions. In some embodiments, the method further

comprises the step of: making the cancer assay panel comprising the plurality of

probes.

[0076] Another aspect of the present description provides a cancer assay panel

made by the method provided herein. In some embodiments, the subset of genomic

regions comprises genomic regions of any one of Lists 1-8. In some embodiments,

the subset of the genomic regions comprises at least 20%, 30%, 40%, 50%, 60%,

70%, 80%, or 90% of the genomic regions in any one of Lists 1-8. In some

embodiments, the subset of the genomic regions comprises at least 500, 1,000,

5000, 10,000 or 15,000 genomic regions in any one of Lists 1-8.

[0077] Another aspect of the present description provides a cancer assay panel

comprising a plurality of probes, wherein each of the plurality of probes is is

the plurality of probes together overlap with at least 90%, 95% or 100% of the

genomic regions in any one of Lists 1-8. In yet another aspect, the present

description provides a cancer assay panel comprising a plurality of probes, wherein

each of the plurality of probes is configured to overlap with one of the genomic

regions in any one of Lists 1-8, and the plurality of probes together overlap with at

least 500, 1,000, 5000, 10,000 or 15,000 genomic regions in any one of Lists 1-8.

5. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] FIGS. 1A-1B illustrate a 2x tiled probe design where, for each strand of

DNA, each base in a target region (boxed in the dotted rectangles) is covered by

(e.g., complementary to) exactly two probes.

[0079] FIG. 2 is a flowchart describing a process of creating a data structure for a

control group, according to an embodiment.

[0080] FIG. 3 is a flowchart describing an additional step of validating the data

structure for the control group of FIG. 2, according to an embodiment.

[0081] FIG. 4 is a flowchart describing a process for selecting genomic regions for

designing probes for a cancer assay panel, according to an embodiment.

[0082] FIG. 5 is an illustration of an example p-value score calculation, according

to an embodiment.

[0083] FIG. 6 is a flowchart describing a process of training a classifier based on

the methylation statuses of fragments, according to an embodiment.

[0084] FIG. 7A is a flowchart describing a process of sequencing a fragment of

cell-free DNA (cfDNA), according to an embodiment.

[0085] FIG. 7B is an illustration of the process of FIG. 7A of sequencing a

fragment of cfDNA to obtain a methylation state vector (e.g., a methylation state

for the fragment), according to an embodiment.

[0086] FIG. 8A is a flowchart of devices for sequencing nucleic acid samples

according to one embodiment.

[0087] FIG. 8B provides an analytic system that analyzes methylation status of

cfDNA cfDNA according accordingto to oneone embodiment. embodiment.

[0088] FIGS. 9A-9B show three graphs of data validating the consistency of

sequencing from a control group.

[0089] FIG. 10 is a graph of the amounts of DNA fragments binding to probes

depending on the sizes of overlaps between the DNA fragments and the probes.

[0090] FIG. 11 compares the numbers of high quality, intermediate quality, and

poor quality probes among the probes targeting hypermethylated fragments

(Hyper) or hypomethylated fragments (Hypo).

[0091] FIG. 12A shows the number, for various assay panels, the number of CpGs

as a function of density.

[0092] FIG. 12B shows, for various assay panels, the G/C fraction as a function of

density.

[0093] FIGS. 13A-13C summarizes frequencies of genomic annotations of

targeted genomic regions (black) and randomly selected regions (white) in the

indicated Assay Panels.

[0094] FIG. 14A depicts a receiver operator curve (ROC) showing the sensitivity

and specificity of cancer detection using Assay Panel 1.

[0095] FIG. 14B is a confusion matrix depicting the accuracy of tissue of origin

(TOO) classifications using Assay Panel 1.

[0096] FIG. 15A depicts a receiver operator curve (ROC) showing the sensitivity

and specificity of cancer detection using Assay Panel 2.

[0097] FIG. 15B is a confusion matrix depicting the accuracy of tissue of origin

(TOO) classifications using Assay Panel 2.

[0098] FIG. 16A depicts a receiver operator curve (ROC) showing the sensitivity

and specificity of cancer detection using Assay Panel 3.

[0099] FIG. 16B is a confusion matrix depicting the accuracy of tissue of origin

(TOO) classifications using Assay Panel 3.

[00100] FIG. 17A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using Assay Panel 4.

[00101] FIG. 17B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using Assay Panel 4.

[00102] FIG. 18A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using Assay Panel 5.

[00103] FIG. FIG. 18B 18B is is aa confusion confusion matrix matrix depicting depicting the the accuracy accuracy of of tissue tissue of of

origin (TOO) classifications using Assay Panel 5.

[00104] FIG. 19A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using Assay Panel 6.

[00105] FIG. 19B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using Assay Panel 6.

[00106] FIG. 20A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using Assay Panel 3A.

[00107] FIG. 20B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using Assay Panel 3A.

[00108] FIG. 21A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using Assay Panel 4A.

[00109] FIG. 21B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using Assay Panel 4A.

[00110] FIG. 22A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using a random selection (subset a)

of 40% of the targeted genomic regions in Assay Panel 3.

[00111] FIG. 22B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using a random selection (subset a) of 40% of the

targeted genomic regions in Assay Panel 3.

[00112] FIG. 23A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using a random selection (subset b)

of 40% of the targeted genomic regions in Assay Panel 3.

[00113] FIG. 23B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using a random selection (subset b) of 40% of the

targeted genomic regions in Assay Panel 3.

[00114] FIG. 24A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using a random selection (subset c)

of 40% of the targeted genomic regions in Assay Panel 3.

[00115] FIG. 24B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using a random selection (subset c) of 40% of the

targeted genomic regions in Assay Panel 3.

[00116] FIG. 25A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using a random selection of 60% of

the targeted genomic regions in Assay Panel 3.

[00117] FIG. 25B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using a random selection of 60% of the targeted

genomic regions in Assay Panel 3.

[00118] FIG. 26A depicts a receiver operator curve (ROC) showing the

sensitivity and specificity of cancer detection using a random selection of 50% of

the targeted genomic regions in Assay Panel 4.

[00119] FIG. 26B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using a random selection of 50% of the targeted

genomic regions in Assay Panel 4.

[00120] FIG. 27A depicts a receiver operator curve (ROC) showing the

the targeted genomic regions in Assay Panel 5.

[00121] FIG. 27B is a confusion matrix depicting the accuracy of tissue of

origin (TOO) classifications using a random selection of 50% of the targeted

genomic regions in Assay Panel 5.

[00122] The figures depict various embodiments of the present description for

purposes of illustration only. One skilled in the art will readily recognize from the

following discussion that alternative embodiments of the structures and methods

illustrated herein may be employed without departing from the principles of the

description described herein.

6. DETAILED DESCRIPTION

6.1. Definitions

[00123] Unless defined otherwise, all technical and scientific terms used herein

have the meaning commonly understood by a person skilled in the art to which this

description belongs. As used herein, the following terms have the meanings

ascribed to them below.

[00124] The term "methylation" as used herein refers to a process by which a

methyl group is added to a DNA molecule. For example, a hydrogen atom on the

pyrimidine ring of a cytosine base can be converted to a methyl group, forming 5-

methylcytosine. methylcytosine. The The term term also also refers refers to to aa process process by by which which aa hydroxymethyl hydroxymethyl group group

is added to a DNA molecules, for example by oxidation of a methyl group on the

pyrimidine ring of a cytosine base. Methylation and hydroxymethylation tend to

occur at dinucleotides of cytosine and guanine referred to herein as "CpG sites.

[00125] In such embodiments, the wet laboratory assay used to detect

methylation may vary from those described herein as is well known in the art.

[00126] The term "methylation site" as used herein refers to a region of a

DNA molecule where a methyl group can be added. "CpG" sites are the most

common methylation site, but methylation sites are not limited to CpG sites. For

example, DNA methylation may occur in cytosines in CHG and CHH, where H is

adenine, cytosine or thymine. Cytosine methylation in the form of 5-

hydroxymethylcytosine may also assessed (see, e.g., WO 2010/037001 and WO

2011/127136, which are incorporated herein by reference), and features thereof,

using the methods and procedures disclosed herein.

[00127] The term "CpG site" as used herein refers to a region of a DNA

molecule where a cytosine nucleotide is followed by a guanine nucleotide in the

linear sequence of bases along its 5' to 3' direction. "CpG" is a shorthand for 5'-C-

phosphate-G-3' that is cytosine and guanine separated by only one phosphate

group. Cytosines in CpG dinucleotides can be methylated to form 5-

methylcytosine.

[00128] The term "CpG detection site" as used herein refers to a region in a

probe that is configured to hybridize to a CpG site of a target DNA molecule. The

CpG site on the target DNA molecule can comprise cytosine and guanine separated

by one phosphate group, where cytosine is methylated or unmethylated. The CpG

site on the target DNA molecule can comprise uracil and guanine separated by one

phosphate group, where the uracil is generated by the conversion of unmethylated

cytosine.

[00129] The The term term "UpG" "UpG" is is aa shorthand shorthand for for 5'-U-phosphate-G-3' 5'-U-phosphate-G-3' that that is is uracil uracil

and guanine separated by only one phosphate group. UpG can be generated by a bisulfite treatment of a DNA that converts unmethylated cytosines to uracils.

Cytosines can be converted to uracils by other methods known in the art, such as

chemical modification, synthesis, or enzymatic conversion.

[00130] The term "hypomethylated" or "hypermethylated" as used herein

refers to a methylation status of a DNA molecule containing multiple CpG sites

(e.g., more than 3, 4, 5, 6, 7, 8, 9, 10, etc.) where a high percentage of the CpG

sites (e.g., more than 80%, 85%, 90%, or 95%, or any other percentage within the

range of 50%-100%) are unmethylated or methylated, respectively.

[00131] The terms "methylation state vector" or "methylation status vector"

as used herein refers to a vector comprising multiple elements, where each element

indicates the methylation status of a methylation site in a DNA molecule

comprising multiple methylation sites, in the order they appear from 5' to 3' in the

DNA DNA molecule. molecule.For example, For Mx, <Mx+1, example, Mx, Mx+2 Mx+1,>,M< >, Mx,< Mx+1, Ux+2 U >, < U, Mx, Mx+1, <Ux, Ux+1, U, U >Ux+2 can> be canmethylation be methylation vectorsfor vectors forDNA DNA molecules molecules comprising comprisingthree three

methylation sites, where M represents a methylated methylation site and U

represents an unmethylated methylation site.

[00132] The term "abnormal methylation pattern" or "anomalous

methylation pattern" as used herein refers to the methylation pattern of a DNA

molecule or a methylation state vector that is expected to be found in a sample less

frequently frequentlythan a threshold than value. a threshold In a particular value. embodiment In a particular provided provided embodiment herein, the herein, the

expectedness of finding a specific methylation state vector in a healthy control

group comprising healthy individuals (e.g. individuals who have not been

diagnosed with cancer) is represented by a p-value. A low p-value score generally

corresponds to a methylation state vector which is relatively unexpected in

comparison to other methylation state vectors within samples from healthy

individuals. A high p-value score generally corresponds to a methylation state

vector which is relatively more expected in comparison to other methylation state

vectors found in samples from healthy individuals in the healthy control group. A

methylation state vector having a p-value lower than a threshold value (e.g., 0.1,

0.01, 0.001, 0.0001, etc.) can be defined as an abnormal/anomalous methylation

pattern. Various methods known in the art can be used to calculate a p-value or

expectedness of a methylation pattern or a methylation state vector. Exemplary

methods provided herein involve use of a Markov chain probability that assumes methylation statuses of CpG sites to be dependent on methylation statuses of neighboring CpG sites. Alternate methods provided herein calculate the expectedness of observing a specific methylation state vector in healthy individuals by utilizing a mixture model including multiple mixture components, each being an independent-sites model where methylation at each CpG site is assumed to be independent independentofof methylation statuses methylation at other statuses CpG sites. at other CpG sites.

[00133] Methods provided herein characterize DNA fragments as anomalous

when they have a methylation pattern that is unusual in comparison to the

methylation patterns of DNA fragments in reference samples, such as samples

from individuals who have not been diagnosed with cancer. The likelihood of a

particular methylation pattern being observed in reference samples can be

represented as a p-value score. Exemplary methods provided herein for modeling

the likelihood of a particular methylation pattern involve use of a Markov chain

probability and a sliding window. If the p-value score falls below a threshold (e.g.,

0.1, 0.01, 0.001, 0.0001, etc.), the DNA fragment having that methylation pattern is

classified as being anomalous. Multiple p-value scores corresponding to

hypermethylated or hypomethylated DNA fragments can be summed or averaged

before being compared to the threshold value. Various methods known in the art

can be adopted to compare p-value scores corresponding to the genomic region and

the threshold value, including but not limited to arithmetic mean, geometric mean,

harmonic mean, median, mode, etc.

[00134] The term "cancerous sample" as used herein refers to a sample

comprising genomic DNAs from an individual diagnosed with cancer. The

genomic DNAs can be, but are not limited to, cfDNA fragments or chromosomal

DNAs from a subject with cancer. The genomic DNAs can be sequenced and their

methylation status can be assessed by methods known in the art, for example,

bisulfite sequencing. When genomic sequences are obtained from public database

(e.g., The Cancer Genome Atlas (TCGA)) or experimentally obtained by

sequencing a genome of an individual diagnosed with cancer, cancerous sample

can refer to genomic DNAs or cfDNA fragments having the genomic sequences.

The term "cancerous samples" as a plural refers to samples comprising genomic

DNAs from multiple individuals, each individual diagnosed with cancer. In

various embodiments, cancerous samples from more than 100, 300, 500, 1,000,

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

10,000, 20,000, 40,000, 50,000, or more individuals diagnosed with cancer are

used.

[00135] The term "non-cancerous sample" as used herein refers to a sample

comprising genomic DNAs from an individual not diagnosed with cancer. The

genomic DNAs can be, but are not limited to, cfDNA fragments or chromosomal

DNAs from a subject without cancer. The genomic DNAs can be sequenced and

their methylation status can be assessed by methods known in the art, for example,

bisulfite sequencing. When genomic sequences are obtained from public database

(e.g., The Cancer Genome Atlas (TCGA)) or experimentally obtained by

sequencing a genome of an individual without cancer, non-cancerous sample can

refer to genomic DNAs or cfDNA fragments having the genomic sequences. The

term "non-cancerous samples" as a plural refers to samples comprising genomic

DNAs from multiple individuals, each individual is not diagnosed with cancer. In

various embodiments, cancerous samples from more than 100, 300, 500, 1,000,

10,000, 20,000, 40,000, 50,000, or more individuals without cancer are used.

[00136] The The term term "training "training sample" sample" as as used used herein herein refers refers to to aa sample sample used used to to

train a classifier described herein and/or to select one or more genomic regions for

cancer diagnosis. The training samples can comprise genomic DNAs or a

modification there of, from one or more healthy subjects and from one or more

subjects having a disease condition for diagnosis (e.g., cancer, a specific type of

cancer, a specific stage of cancer, etc.). The genomic DNAs can be, but are not

limited to, cfDNA fragments or chromosomal DNAs. The genomic DNAs can be

sequenced and their methylation status can be assessed by methods known in the

art, for example, bisulfite sequencing. When genomic sequences are obtained from

public database (e.g., The Cancer Genome Atlas (TCGA)) or experimentally

obtained by sequencing a genome of an individual, a training sample can refer to

genomic DNAs or cfDNA fragments having the genomic sequences.

[00137] The term "test sample" as used herein refers to a sample from a

subject, whose health condition was, has been or will be tested using a classifier

and/or an assay panel described herein. The test sample can comprise genomic

DNAs or a modification there of. The genomic DNAs can be, but are not limited

to, cfDNA fragments or chromosomal DNAs.

[00138] The term "target genomic region" as used herein refers to a region in

a genome selected for selected for analysis in test samples. An assay panel is

generated with probes designed to hybridize to (and optionally pull down) nucleic

acid fragments derived from the target genomic region or a fragment thereof. A

nucleic acid nucleic acidfragment derived fragment from from derived the target genomicgenomic the target region refers regiontorefers a nucleic to a nucleic

acid fragment generated by degradation, cleavage, bisulfite conversion, or other

processing of the DNA from the target genomic region.

[00139] The term "off-target genomic region" as used herein refers to a region

in a genome which has not been selected for analysis in test samples, but has

sufficient homology to a target genomic region to potentially be bound and pulled

down by a probe designed to target the target genomic region. In one embodiment,

an off-target genomic region is a genomic region that aligns to a probe along at

least 45 bp with at least a 90% match rate.

[00140] The terms "converted DNA molecules," "converted cfDNA

molecules," and "modified fragment obtained from processing of the cfDNA

molecules" refer to DNA molecules obtained by processing DNA or cfDNA

molecules in a sample for the purpose of differentiating a methylated nucleotide

and an unmethylated nucleotide in the DNA or cfDNA molecules. For example, in

one embodiment, the sample can be treated with bisulfite ion (e.g., using sodium

bisulfite), as is well-known in the art, to convert unmethylated cytosines ("C") to

uracils ("U"). In another embodiment, the conversion of unmethylated cytosines to

uracils is accomplished using an enzymatic conversion reaction, for example, using

a cytidine deaminase (such as APOBEC). After treatment, converted DNA

molecules or cfDNA molecules include additional uracils which are not present in

the original cfDNA sample. Replication by DNA polymerase of a DNA strand

comprising a uracil results in addition of an adenine to the nascent complementary

strand instead of the guanine normally added as the complement to a cytosine or

methylcytosine.

[00141] The term "cell free nucleic acid," "cell free DNA," or "cfDNA" refers

to nucleic acid fragments that circulate in an individual's body (e.g., bloodstream)

and originate from one or more healthy cells and/or from one or more cancerous

cells. Additionally, cfDNA may come from other sources such as viruses, fetuses,

etc.

40

[00142] The term "circulating tumor DNA" or "ctDNA" refers to nucleic acid

fragments that originate from tumor cells, which may be released into an

individual's bloodstream as result of biological processes such as apoptosis or

necrosis of dying cells or actively released by viable tumor cells.

[00143] The term "fragment" as used herein can refer to a fragment of a

nucleic acid molecule. For example, in one embodiment, a fragment can refer to a

cfDNA molecule in a blood or plasma sample, or a cfDNA molecule that has been

extracted from a blood or plasma sample. An amplification product of a cfDNA

molecule may also be referred to as a "fragment." In another embodiment, the term

"fragment" refers to a sequence read, or set of sequence reads, that have been

processed processedfor forsubsequent analysis subsequent (e.g., analysis for infor (e.g., machine-learning based in machine-learning based

classification), as described herein. For example, as is well known in the art, raw

sequence reads can be aligned to a reference genome and matching paired end

sequence reads assembled into a longer fragment for subsequent analysis.

[00144] The term "individual" refers to a human individual. The term

"healthy individual" refers to an individual presumed not to have a cancer or

disease.

[00145] The The term term "subject" "subject" refers refers to to an an individual individual whose whose DNA DNA is is being being

analyzed. A subject may be a test subject whose DNA is be evaluated using a

targeted panel as described herein to evaluate whether or not the person has cancer

or another disease. A subject may also be part of a control group known not to have

cancer or another disease. A subject may also be part of a cancer or other disease

group known to have cancer or another disease. Control and cancer/disease groups

may be used to assist in designing or validating the targeted panel.

[00146] The term "sequence reads" as used herein refers to nucleotide

sequences reads from a sample. Sequence reads can be obtained through various

methods provided herein or as known in the art.

[00147] The term "sequencing depth" as used herein refers to the count of the

number of times a given target nucleic acid within a sample has been sequenced

(e.g., the count of sequence reads at a given target region). Increasing sequencing

depth can reduce required amounts of nucleic acids required to assess a disease

state (e.g., cancer or cancer tissue of origin).

[00148] The term "tissue of origin" or "TOO" as used herein refers to the

organ, organ group, body region or cell type that cancer arises or originates from.

The identification of a tissue of origin or cancer cell type typically allows for

identification of the most appropriate next steps in the care continuum of cancer to

further diagnose, stage, and decide on treatment.

[00149] The term "transition" generally refers to changes in base composition

from one purine to another purine, or from one pyrimidine to another pyrimidine.

For instance, For instance, the the following following changes changes are transitions: are transitions: C->U, U C,A,G-> C-U, UC, A G, G, G C T,and CT, and T T >C C.

[00150] "An entirety of probes" of a panel or bait set or "an entirety of

polynucleotide-containing polynucleotide-containing probes" probes" of of aa panel panel or or bait bait set set generally generally refers refers to to all all of of

the probes delivered with a specified panel or bait set. For instance, in some

embodiments, a panel or bait set may include both (1) probes having features

specified herein (e.g., probes for binding to cell-free DNA fragments

corresponding to or derived from genomic regions set forth herein in one or more

Lists) and (2) additional probes that do not contain such feature(s). The entirety of

probes of a panel generally refers to all probes delivered with the panel or bait set,

including such probes that do not contain the specified feature(s).

6.2. Other interpretational conventions

[00151] Ranges recited herein are understood to be shorthand for all of the

values within the range, inclusive of the recited endpoints. For example, a range of

1 to 50 is understood to include any number, combination of numbers, or sub-range

from the group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,

40, 41, 42, 43, 44, 45, 46, 47, 48, 49, and 50.

6.3. Cancer assay panel

[00152] In a first aspect, the present description provides a cancer assay panel

(e.g., a bait set) comprising a plurality of probes. The probes can be

polynucleotide-containing polynucleotide-containing probes probes that that are are specifically specifically designed designed to to target target one one or or

more nucleic acid molecules corresponding to or derived from genomic regions

that are differentially methylated in cancer compared to non-cancer samples as identified by methods provided herein. The probes are used as baits to collect cfDNA derived from target genomic regions that are differentially methylated in cancer. In a diagnostic assay, enrichment increases sensitivity and accuracy while reducing sequencing expenses.

[00153] For designing the cancer assay panel, an analytics system may collect

samples corresponding to various outcomes under consideration, e.g., samples

known to have cancer, samples considered to be healthy, samples from a known

tissue of origin, etc. The sources of the cfDNA and/or ctDNA used to select target

genomic regions can vary depending on the purpose of the assay. For example,

different sources may be desirable for an assay intended to diagnose cancer

generally, a specific type of cancer, a cancer stage, or a tissue of origin. These

samples may be processed with whole-genome bisulfite sequencing (WGBS) or or

obtained from public database (e.g., TCGA). The analytics system may be any

generic computing system with a computer processor and a computer-readable

storage medium with instructions for executing the computer processor to perform

any or all operations described in this present disclosure.

[00154] The analytics system may then select target genomic regions based on

methylation patterns of nucleic acid fragments. One approach considers pairwise

distinguishability between pairs of outcomes for regions or more specifically one

or more CpG sites. Another approach considers distinguishability for regions or

more specifically one or more CpG sites when considering each outcome against

the remaining outcomes. From the selected target genomic regions with high

distinguishability power, the analytics system may design probes to target nucleic

acid fragments inclusive of the selected genomic regions. The analytics system

may generate variable sizes of the cancer assay panel, e.g., where a small sized

cancer assay panel includes probes targeting the most informative genomic region,

a medium sized cancer assay panel includes probes from the small sized cancer

assay panel and additional probes targeting a second tier of informative genomic

regions, and a large sized cancer assay panel includes probes from the small sized

and the medium sized cancer assay panels and even more probes targeting a third

tier tier of ofinformative informativegenomic regions. genomic With such regions. With cancer assay panels, such cancer assay the analytics panels, the analytics

system may train classifiers with various classification techniques to predict a

PCT/US2019/053509

sample's likelihood of having a particular outcome, e.g., cancer, specific cancer

type, other disorder, etc.

[00155] Target genomic regions may be selected to maximize classification

accuracy, subject to a size limitation (which is determined by sequencing budget

and desired depth of sequencing). Potential target genomic regions can be ranked

according to their tentative classification potential as described herein and then

greedily added to a panel until the size limitation is reached.

[00156] cancer assay A cancer assay panel panel can can be be used used to to detect detect the the presence presence or or absence absence of of A cancer generally, the stage (e.g., I, II, III or IV) of cancer, and/or the tissue of

origin of a cancer. Depending on the purpose, a panel can include probes targeting

genomic regions differentially methylated between general cancerous (pan-cancer)

samples and non-cancerous samples. In some embodiments, a cancer assay panel is

designed based on bisulfite sequencing data generated from the cfDNA and/or

whole genomic DNA of a set of cancer and non-cancer individuals.

[00157] Each probe, probe pair, or probe set can be designed to target, by

selective hybridization, one or more target genomic regions. The target genomic

regions are selected based on several criteria designed to enhance selective

enrichment of relevant cfDNA fragments while decreasing noise and non-specific

binding. For example, a panel can include probes that can selectively hybridize to

(i.e., bind to) and enrich cfDNA fragments that are differentially methylated in

cancerous samples. Furthermore, the probes may be further designed to target

genomic regions that are determined to have an anomalous methylation pattern

comprising hypermethylation or hypomethylation. In some embodiments, a panel

comprises both a first set of probes targeting hypermethylated fragments and a

second set of probes targeting hypomethylated fragments. In some embodiments,

the ratio between the first set of probes targeting hypermethylated fragments and

the second set of probes targeting hypomethylated fragments (hyper:hypo ratio)

ranges between 0.4 and 2, between 0.5 and 1.8, between 0.5 and 1.6, between 1.4

and 1.6, between 1.2 and 1.4, between 1 and 1.2, between 0.8 and 1, between 0.6

and 0.8 or between 0.4 and 0.6.

[00158] Sequencing of the enriched fragments provides information relevant to

a diagnosis of cancer. Methods of identifying fragments having abnormal methylation patterns are provided in detail in Section 6.4.2 ("Anomalously methylated fragments") and methods of identifying fragments having hypomethylation or hypermethylation patterns are provided in detail in Section

6.4.3 ("Analysis of hyper or hypomethylated fragments").

[00159] For example, genomic regions can be selected when cfDNA and/or

ctDNA fragments that align with a genomic region have a methylation pattern with

a low p-value according to a Markov model trained on a set of non-cancerous

samples. cfDNA and/or ctDNA fragments used to select genomic regions may

additionally be required to contain at least a threshold number (e.g., 5) CpG's, and

70%, 75%, 80%, 85%, 90%, 95%, 98%, or 100% of the CPG sites may be required

to be either methylated or unmethylated. Each of the probes can target at least

25bp, 30bp, 35bp, 40bp, 45bp, 50bp, 60bp, 70bp, 80bp, 90bp, 100bp, 110bp, or

120bp of a genomic region. In some embodiments, the genomic regions is required

to have less than 20, 15, 10, 8, or 6 methylation sites.

[00160] In some embodiments, target genomic region selection involves a

calculation performed with respect to each CpG site. Specifically, a first count,

ncancer, is determined from the number of cancer-containing samples that include an

abnormally hypermethylated and/or hypomethylated cfDNA and/or ctDNA

fragment overlapping that CpG site, and a second count, nnon-cancer, is determined

from the number of healthy samples that include an abnormally hypermethylated

and/or hypomethylated cfDNA and/or ctDNA fragment overlapping that CpG site.

Genomic regions can be selected based on criteria positively correlated Ncancer ncancer and

inversely correlated with nnon-cancer. In other embodiments, target genomic region

selection involves a calculation performed with respect to a plurality of CpG sites.

[00161] In some embodiments, the number of non-cancerous samples (nnon- (n-

cancer) and the number of cancerous samples (ncancer) having an abnormally

hypermethylated and/or hypermethylated hypomethylated and/or cfDNAcfDNA hypomethylated and/orand/or ctDNA fragment ctDNA fragment

overlapping a CpG site are counted. A ranking score is then calculated for the CpG

site. In some embodiments, the ranking score is equal to (ncancer + 1) / (ncancer + Nnon- nnon-

cancer 2). CpG + 2). sites CpG ranked sites by by ranked this metric this are metric greedily are added greedily to to added a panel until a panel the until the

panel size budget is exhausted. The process of selecting genomic regions indicative

of cancer is further detailed herein..

[00162] Further filtration can be performed to select probes with high

specificity for enrichment (i.e., high binding efficiency) of nucleic acids derived

from targeted genomic regions. Probes can be filtered to reduce non-specific

binding (or off-target binding) to nucleic acids derived from non-targeted genomic

regions. In some embodiments, further filtration is performed to select probes for a

cancer assay panel that will not pull down off-target genomic regions. In some

embodiments, probes that can pull down off-target genomic regions are eliminated.

In some embodiments, a threshold is set to define probes with an unacceptably

high risk of off-target effects. In one embodiment, probes with more than 80%,

85%, 90%, 95% or 98% identity to a threshold number (e.g., 1, 2, 3, 4, 5, 10, 15,

20, 25 or more) of off-target genomic regions are eliminated from a panel. In some

embodiments, probes are eliminated from a cancer assay panel if they comprise a

sequence of at least 20, 25, 30, 35, 40, 45, 50, 55, or 60 nt with more than 80%,

20, 25 or more) of off-target genomic regions. Filtering removes repetitive probes

that can pull down off-target fragments, which are not desired and can impact

assay efficiency.

[00163] A fragment-probe overlap of at least 45 bp enables a non-negligible

amount of pulldown under certain hybridization conditions. Thus, in some

embodiments, probes are eliminated from a cancer panel if they comprise a

sequence of at least 45 nt with more than 80%, 85%, 90%, 95% or 98% identity to

1, 2, 3, 4, 5, 10, 15, 20, 25 or more off-target genomic regions.

[00164] In some embodiments, the probes of a cancer assay panel are designed

to hybridize to converted cfDNA derived from target genomic regions. After

hybridization, target polynucleotides can be recovered and/or isolated, optionally

amplified, and sequenced by any suitable method. The sequence reads provide

information relevant for detection of cancer, diagnosis of cancer, and assessment of

a cancer tissue of origin or type of cancer. To this end, a panel may be designed to

include a plurality of probes that can capture fragments that can together provide

information relevant to cancer detection and diagnosis of cancer. In some

embodiments, a panel includes at least 50, 60, 70, 80, 90, 100, 120, 150, or 200

different pairs of probes. In other embodiments, a panel includes at least at least

500, 1,000, 2,000, 2,500, 5,000, 10,000, 12,000, 15,000, 20,000, 30,000, 40,000,

50,000, or 100,000 probes. The plurality of probes together can comprise at least

20,000, 30,000, 40,000, 50,000, 75,000, 0.1 million, 0.2 million, 0.4 million, 0.6

million, 0.8 million, 1 million, 2 million, 3 million, 4 million, 5 million, 6 million,

7 million, 8 million, 9 million, or 10 million nucleotides. Optionally, the sequence

at one or both ends of a probe overlaps with the sequence of other probes targeting

the same genomic region or an adjacent genomic region.

[00165] Specifically, in some embodiments, the cancer assay panel comprises

at least 50 pairs of probes, wherein each pair of the at least 50 pairs comprises two

probes configured to overlap each other by an overlapping (e.g., identical)

sequence, wherein the overlapping sequence comprises a 30-nucleotide sequence,

and wherein the 30-nucleotide sequence is configured to hybridize to a modified

fragment obtained from processing of the cfDNA molecules corresponding to one

or more genomic regions, wherein each of the genomic regions comprises at least

five methylation sites, and wherein the at least five methylation sites have an

anomalous methylation pattern in training samples. In other words, when cfDNA

molecules in training samples corresponding to the genomic region are analyzed,

they have methylation status vectors appearing less frequently than a threshold

value in reference samples.

[00166] In other embodiments, the cancer assay panel comprises at least 500

pairs of probes, wherein each pair of the at least 500 pairs comprises two probes

configured to overlap each other by an overlapping sequence, wherein the

overlapping sequence comprises a 30-nucleotide sequence, and wherein the 30-

nucleotide sequence is configured to hybridize to a modified fragment obtained

from processing of the cfDNA molecules corresponding to one or more genomic

regions, wherein each of the genomic regions comprises at least five methylation

sites, and wherein the at least five methylation sites have an anomalous

methylation pattern in training samples. Again, when cfDNA molecules in training

samples corresponding to the genomic region are analyzed, they have methylation

status vectors appearing less frequently than a threshold value in reference

samples.

[00167] In a preferred embodiment, the at least five methylation sites are

differentially methylated either between cancerous and non-cancerous samples or

between one or more pairs of samples from different cancer types. In some embodiments, the converted cfDNA molecules comprise cfDNA molecules treated

(e.g., via bisulfite treatment or enzymatic conversion) to convert unmethylated C

(cytosine) to U (uracil). In some cases, the uracil is further converted to thymine

(e.g., upon PCR amplification).

[00168] The selected target genomic regions are optionally located in various

positions in a genome, including but not limited to promoters, enhancers, exons,

introns, and intergenic regions. In certain embodiments, the target genomic

regions comprise genes and/or transcriptional control regions that are differentially

expressed in cancer versus non-cancer, in different stages of cancer, and/or in

cancers arising from different tissues of origin.

[00169] A cancer assay panel designed by methods provided herein may

comprise at least 1,000 pairs of probes, each pair of which comprises two probes

configured to overlap each other by an overlapping sequence comprising a 30-

nucleotide fragment. The 30-nucleotide fragment comprises at least three, at least

four, at least five of more CpG sites, wherein at least 70%, at least 80%, or at least

90% of the at least three, at least four, at least five, or more CpG sites are either

CpG or UpG. The 30-nucleotide fragment is configured to bind to one or more

cfDNA fragments derived from genomic regions in cancerous samples, wherein the

one or more genomic regions have at least three, at least four, at least five or more

methylation sites with an abnormal methylation pattern compared to DNA

molecules from a non-cancer sample.

[00170] Another cancer assay panel comprises at least 2,000 probes, each of

which is designed as a hybridization probe complementary to one or more genomic

regions. Each of the genomic regions is selected based on the criteria that it

comprises (i) at least 30 nucleotides, and (ii) at least three, at least four, at least

five, or more methylation sites, wherein the at least five methylation sites have an

abnormal methylation pattern and are either hypomethylated or hypermethylated.

[00171] In some instances, primers may be used to specifically amplify

targets/biomarkers of interest (e.g., by PCR), thereby enriching the sample for

desired targets/biomarkers (optionally without hybridization capture). For example,

forward and reverse primers can be prepared for each genomic region of interest

and used to amplify fragments that correspond to or are derived from the desired genomic region. Thus, while the present disclosure pays particular attention to cancer assay panels and bait sets for hybridization capture, the disclosure is broad enough to encompass other methods for enrichment of cell-free DNA.

Accordingly, a skilled artisan, with the benefit of this disclosure, will recognize

that methods analogous to those described herein in connection with hybridization

capture can alternatively be accomplished by replacing hybridization capture with

some other enrichment strategy, such as PCR amplification of cell-free DNA

fragments that correspond with genomic regions of interest. In some embodiments,

bisulfite padlock probe capture is used to enrich regions of interest, such as is

described in Zhang et al. (US 2016/0340740). In some embodiments, additional or

alternative methods are used for enrichment (e.g., non-targeted enrichment) such as

reduced representation bisulfite sequencing, methylation restriction enzyme

sequencing, methylation DNA immunoprecipitation sequencing, methyl-CpG-

binding domain protein sequencing, methyl DNA capture sequencing, or

microdroplet PCR.

6.3.1. Probes

[00172] Cancer assay panels provided herein may include a panel having a set

of hybridization probes (also referred to herein as "probes") designed to enrich

selected target selected target genomic genomic regions regions by pulling by pulling down nucleic down nucleic acid fragments acid fragments of interest. of interest.

The probes are designed to interrogate the methylation status of target genomic

regions (e.g., of a human or other organism) that are suspected to correlate with the

presence or absence of cancer generally, cancer stage, and/or tissue of origin.

[00173] The probes can be designed to anneal (or hybridize) to a target

(complementary) strand of DNA or RNA. The target strand can be the "positive"

strand (e.g., the strand transcribed into mRNA and subsequently translated into a

protein) or the complementary "negative" strand. In a particular embodiment, a

cancer assay panel includes two sets of probes for a target genomic region, one

targeting the positive strand and the other targeting the negative strand. In certain

embodiments, probes targeting different strands of a DNA sequence can have

regions of sequence complementarity to each other.

[00174] Probes are optionally designed to hybridize to native DNA or to

converted DNA. cfDNA, ctDNA and/or chromosomal DNA can be converted by various methods known in the art to preserve epigenetic marks such as methylation. Optionally, DNA is converted by bisulfite or enzymatic treatment, which converts unmethylated cytosines into uracils. Thus, the methylation pattern determines which cytosines in a CpG sequence are converted to uracil.

Unmethylated CpG sites in a target region are converted by bisulfite (or enzymatic

means) to UpG sites, If the target DNA is amplified after conversion by a process

employing DNA polymerase, UpG sequences are further transformed to TpG

sequences, and a complementary probe would have a CpA sequence. Methylated

CpG sites in a target region retain their CpG sequence, SO so a complementary probe

would also have a CpG sequence. Cytosines that are not at a CpG sequences are

generally not methylated. These cytosines are generally converted to uracils,

which are further transformed to thymidine after amplification, SO so probes

complementary to bisulfite converted DNA comprise an A for each converted C

that is not in a CpG site.

[00175] Probes are optionally designed under an assumption that all CpG sites

are methylated in some target genomic regions (perfect hypermethylation),

whereas no CpG sites are methylated in other target genomic regions (perfect

hypomethylation). Stated differently, probes designed to target a hypomethylated

region may be designed to be complementary to a region in which all CpG sites

have been converted UpGs, whereas probes designed to target a hypermethylated

region may be designed to be complementary to a region in which none of the CpG

sites sites have havebeen converted. been converted.

[00176] Since the probes may be configured to hybridize to a converted DNA

or converted cfDNA molecule derived from one or more genomic regions, the

probes can have a sequence different from the targeted genomic region. For

example, a DNA molecule containing unmethylated CpG site will be converted to

include UpG because unmethylated cytosines are converted to uracils by a

conversion reaction (e.g., bisulfite treatment). As a result, a probe is configured to

hybridize to a sequence including UpG instead of a naturally existing unmethylated

CpG. Accordingly, a complementary site in the probe to the unmethylated site can

comprise CpA instead of CpG, and some probes targeting a hypomethylated site

where all methylation sites are unmethylated can have no guanine (G) bases. In

some embodiments, at least 3%, 5%, 10%, 15%, 20%, 30%, or 40% of the probes lack G (Guanine). In some embodiments, at least 80, 85, 90, 92, 95, 98% of the probes on the panel have exclusively either CpG or CpA on CpG detection sites.

Accordingly, in some embodiments, polynucleotide-containing probes have a

nucleic acid sequence that is either (1) identical in sequence to a sequence within a

target genomic region (e.g., target genomic regions set forth herein in any one of

Lists 1-8) or (2) varies with respect to a sequence within the genomic region only

one or more transitions (e.g., changes in base composition at a site due to bisulfite

conversion or other conversion techniques), wherein each respective transition in

the one or more transitions occurs at a nucleotide corresponding to a CpG site in

the genomic region.

[00177] In some embodiments, probes on the panel comprise less than 20, 15,

10, 8, or 6 CpG detection sites. In some embodiments, probes on the panel

comprise more than 5, 6, 7, 8, 9, or 10 CpG detection sites.

[00178] In some embodiments, probes are conjugated to a tag (e.g., a non-

nucleic acid affinity moiety), such as a biotin moiety. In some embodiments,

probes are affixed to a solid support, such as an array.

[00179] A cancer assay panel that interrogates methylation status can also be

designed to interrogate other genetic or epigenetic marks that are different between

cfDNA in cancer and healthy samples. In some embodiments, probes are designed

to enrich for all cfDNA from a particular target region regardless of the

methylation status of the cfDNA molecule. This might be because the target

genomic region is not highly methylated or unmethylated, or for the purpose of

targeting small mutations or SCNAs rather than methylation changes.

[00180] The probes optionally range in length from 10s to 100s of nucleotides.

The probes can comprise at least 20, 30, 50, 75, 100, or 120 nucleotides. The

probes can have a length of less than 300, 250, 200, or 150 nucleotides. In some

embodiments, the probes comprise 100-150 nucleotides. In one particular

embodiment, the probes are 120 nucleotides in length.

[00181] In some embodiments, the probes are designed in a "2x tiled" fashion

to cover overlapping portions of a target region. Each probe optionally overlaps in

coverage at least partially with another probe in the library. In such embodiments,

the panel contains multiple pairs of probes, with each probe in a pair overlaping the other by at least 25, 30, 35, 40, 45, 50, 60, 70, 75 or 100 nucleotides. In some embodiments, the overlapping sequence can be designed to be complementary to a target genomic region (or cfDNA derived therefrom) or to be complementary to a sequence with homology to a target region or cfDNA. Thus, in some embodiments, at least two probes are complementary to the same sequence within a target genomic region, and a nucleotide fragment corresponding to or derived from the target genomic region can be bound and pulled down by at least one of the probes.

Other levels of tiling are possible, such as tiling, 4x tiling, 3x tiling, etc., 4x tiling, wherein etc., each wherein each

nucleotide in a target region can bind to more than two probes.

[00182] In one one embodiment, embodiment, each each base base in in aa target target genomic genomic region region is is overlapped overlapped

by exactly two probes, as illustrated in FIG. 1. A single pair of probes is enough to

pull down a genomic region if the overlap between the two probes is longer than

the target genomic region and extends beyond both ends of the target genomic

region. In some instances, even relatively small target regions may be targeted with

three probes (see FIG. 1A). A probe set comprising three or more probes is

optionally used to capture a larger genomic region. See FIG. 1B. In some

embodiments, subsets of probes will collectively extend across an entire genomic

region (e.g., may be complementary to non-converted or converted fragments from

the genomic region). A tiled probe set optionally comprises probes that collectively

include include at at least least two two probes probes that that overlap overlap every every nucleotide nucleotide in in the the genomic genomic region. region.

FIGS. 1A and 1B. This is done to ensure that cfDNAs comprising a small portion

of a target genomic region at one end will have a substantial overlap extending into

the adjacent non-targeted genomic region with at least one probe, to provide for

efficient capture.

[00183] For example, a 100 bp cfDNA fragment comprising a 30 nt target

genomic region can be guaranteed to have at least 65 bp overlap with at least one

of the overlapping probes. Other levels of tiling are possible. For example, to

increase target size and add more probes in a panel, probes can be designed to

expand a 30 bp target region by at least 70 bp, 65 bp, 60 bp, 55 bp, or 50 bp. To

capture any fragment that overlaps the target region at all (even if by only 1bp), the

probes can be designed to extend past the ends of the target region on either side.

[00184] In one one particular particular embodiment, embodiment, the the smallest smallest target target genomic genomic region region is is

30bp. When a new CpG site is added to the panel (based on the greedy selection as described above), a new target of 30 nt is centered on the CpG site of interest.

Then, it is checked whether each edge of this new target is close enough to other

targets such that they can be merged. Merging avoids a panel comprising close but

distinct targets with overlapping probes. This is based on a "merge distance"

parameter which extends about 100 nt, 150 nt, 200 nt, 250 nt, or 300 nt on either

side of every target region. Merger creates a larger target genomic region. If a new

CpG site is merged into targets on both sides, the number of target genomic regions

is reduced.

[00185] In some embodiments, an assay panel provided herein comprises a

plurality of polynucleotide probes configured to hybridize to a modified fragment

obtained from processing of the cfDNA molecules, wherein each of the cfDNA

molecules corresponds to or is derived from one or more genomic regions. In some

embodiments, at least 15%, 20%, 30%, or 40% of the genomic regions are in exons

or introns. In some embodiments, at least 5%, 10%, 15%, 20%, 30% or 40% of the

genomic regions are in exons. In some embodiments, less than 5%, 10%, 15%,

20%, 25%, or 30% of the genomic regions are in intergenic regions.

[00186] In some embodiments, the entire probes on the panel together are

configured to hybridize to modified fragments obtained from the cfDNA molecules

corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%,

90% or 95% of the genomic regions in one or more of Lists 1-8. In some

embodiments, the entire probes on the panel together are configured to hybridize to

modified fragments obtained from the cfDNA molecules corresponding to or

derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the

genomic regions in one or more of the genomic regions in List 1. In some

modified fragments obtained from the cfDNA molecules corresponding to or

derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the

genomic regions in one or more of the genomic regions in List 2. In some

modified fragments obtained from the cfDNA molecules corresponding to or

derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the

genomic regions in one or more of the genomic regions in List 3. In some

embodiments, the entire probes on the panel together are configured to hybridize to modified fragments obtained from the cfDNA molecules corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in one or more of the genomic regions in List 5. In some embodiments, the entire probes on the panel together are configured to hybridize to modified fragments obtained from the cfDNA molecules corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in one or more of the genomic regions in List 7. In some embodiments, the entire probes on the panel together are configured to hybridize to modified fragments obtained from the cfDNA molecules corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in one or more of the genomic regions in List 8. In some embodiments, the entire probes on the panel together are configured to hybridize to modified fragments obtained from the cfDNA molecules corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in one or more of the genomic regions in List 4. In some embodiments, the entire probes on the panel together are configured to hybridize to modified fragments obtained from the cfDNA molecules corresponding to or derived from at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95% of the genomic regions in one or more of the genomic regions in List 6. In some embodiments, the entire probes on the panel together are configured to hybridize to modified fragments obtained from the cfDNA molecules corresponding to or derived from at least 500, 1,000, 5000, 10,000 or 15,000 genomic regions in one or more of Lists 1-8.

6.4. Methods of selecting target genomic regions

[00187] In another aspect, methods are provided for identifying anomalously

methylated DNA molecules from a sample. In some embodiments, the methods

disclosed herein can be used to select target genomic regions for detecting cancer,

assessing a cancer tissue of origin, or type of cancer. The targeted genomic regions

can be used to design and manufacture probes for a cancer assay panel.

Methylation status of the target genomic regions can be screened using the cancer

assay panel. In other embodiments, these methods can be used as part of a filtering

process to limit a data set (e.g., a sequencing data set) to reduce subsequent processing or analysis requirements. For example, the methods disclosed herein can be used to set a threshold for anomalously methylated fragments, which are likely derived from cancer or cancer cells, and the threshold used as a to filter out sequence reads or fragments that don't meet the threshold, and thus, are more likely derived from healthy cells.

6.4.1. Generation of data structure

[00188] FIG. FIG. 22 is is aa flowchart flowchart describing describing aa process process 200 200 of of generating generating aa data data

structure for a healthy control group, according to an embodiment. To create a

healthy control group data structure, the analytics system receives sequences from

a plurality of DNA fragments (e.g., cfDNA and/or ctDNA) from a plurality of

healthy subjects. A methylation state vector is identified for each fragment, for

example via the process 100.

[00189] The analytics system subdivides 210 the methylation state vector of

each DNA fragment into strings of CpG sites. In one embodiment, the analytics

system subdivides 210 the methylation state vector such that the resulting strings

are all less than a given length. For example, dividing a methylation state vector of

length 11 may be subdivided into strings of length less than or equal to 3 would

result in 9 strings of length 3, 10 strings of length 2, and 11 strings of length 1. In

another example, a methylation state vector of length 7 being subdivided into

strings of length less than or equal to 4 would result in 4 strings of length 4, 5

strings of length 3, 6 strings of length 2, and 7 strings of length 1. If the

methylation state vector resulting from a DNA fragment is shorter than or the same

length as the specified string length, then the methylation state vector may be

converted into a single string containing all CpG sites of the vector.

[00190] The analytics system tallies 220 the strings by counting, for each

possible CpG site and possibility of methylation states in the vector, the number of

strings present in the control group having the specified CpG site as the first CpG

site in the string and having that possibility of methylation states. For a string

length of three at a given CpG site, there are 2^3 or 8 possible string

configurations. For each CpG site, the analytics system tallies 220 how many

occurrences of each possible methylation state vector appear up in the control

group. This may involve tallying the following quantities: Mx, Mx+1, < Mx, M, MMx+2 >, <>, <

Mx, Mx, Mx+1, M, U Ux+2 U, U, <U Ux, >, < >, Ux+1,each > for Ux+2 starting > for each starting CpG site CpGin site in reference the the reference

genome. The analytics system creates 230 a data structure storing the tallied counts

for each string possibility at each starting CpG.

[00191] There are several benefits to setting an upper limit on string length.

First, depending on the maximum length for a string, the size of the data structure

created by the analytics system can dramatically increase in size. For instance, a

maximum string length of 4 means that there are at most 2^4 numbers to tally at

every CpG. Increasing the maximum string length to 5 doubles the possible

number of methylation states to tally. Reducing string size helps reduce the

computational and data storage burden of the data structure. In some embodiments,

the string size is 3. In some embodiments, the string size is 4. A second reason to

limiting the maximum string length is to avoid overfitting downstream models.

Calculating probabilities based on large strings of CpG sites can be problematic if

the long CpG strings do not have a strong biological effect on the outcome (e.g.,

predictions of anomalousness that predictive of the presence of cancer), as it

requires a significant amount of data that may not be available, and would thus be

too sparse for a model to perform appropriately. For example, calculating a

probability of anomalousness/cancer conditioned on the prior 100 CpG sites would

require counts of strings in the data structure of length 100, ideally some matching

exactly the prior 100 methylation states. If only sparse counts of strings of length

100 are available, there will be insufficient data to determine whether a given

string of length of 100 in a test sample is anomalous or not.

6.4.1. Validation of data structure

[00192] Once the data structure has been created, the analytics system may

seek to validate 240 the data structure and/or any downstream models making use

of the data structure.

[00193] This first type of validation ensures that potential cancerous samples

are removed from the healthy control group SO so as to introduce bias into the healthy

data structure. This type of validation checks consistency within the control

group's data structure. For example, the healthy control group may contain a

sample from an individual with an undiagnosed cancer that contains a plurality of

anomalously methylated fragments. The analytics system may perform various

PCT/US2019/053509

calculations to determine whether to exclude data from a subject with apparently

undiagnosed cancer.

[00194] A second type of validation checks the probabilistic model used to

calculate p-values with the counts from the data structure itself (i.e., from the

healthy control group). A process for p-value calculation is described below in

conjunction with FIG. 5. Once the analytics system generates a p-value for the

methylation state vectors in the validation group, the analytics system builds a

cumulative density function (CDF) with the p-values. With the CDF, the analytics

system may perform various calculations on the CDF to validate the control

group's data structure. One test uses the fact that the CDF should ideally be at or

below an identity function, such that CDF(x) <X. X.On Onthe theconverse, converse,being beingabove abovethe the

identity function reveals some deficiency within the probabilistic model used for

the control group's data structure. For example, if 1/100 of fragments have a p-

value score of 1/1000 meaning CDF(1/1000) = 1/100 > 1/1000, then the second

type of validation fails indicating an issue with the probabilistic model. See e.g.,

U.S. Appl. No. 16/352,602, published as U.S. Publ. No. 2019/0287652, which is

hereby incorporated by reference in its entirety.

[00195] A third type of validation uses a healthy set of validation samples

separate from those used to build the data structure. This tests if the data structure

is properly built and the model works. An exemplary process for carrying out this

type of validation is described below in conjunction with FIG. 3. The third type of

validation can quantify how well the healthy control group generalizes the

distribution of healthy samples. If the third type of validation fails, then the healthy

control group does not generalize well to the healthy distribution.

[00196] A fourth type of validation tests with samples from a non-healthy

validation group. The analytics system calculates p-values and builds the CDF for

the non-healthy validation group. With a non-healthy validation group, the

analytics systems expects to see the CDF(x) > x for at least some samples or, stated

differently, the converse of what was expected in the second type of validation and

the third type of validation with the healthy control group and the healthy

validation group. If the fourth type of validation fails, then this is indicative that the

model is not appropriately identifying the anomalousness that it was designed to

identify.

[00197] FIG. FIG. 33 is is aa flowchart flowchart describing describing an an additional additional step step 240 240 of of validating validating the the

data structure for the control group of FIG. 2, according to an embodiment. In this

step 240 of validating the data structure, the analytics system performs the fourth

type of validation test as described above which utilizes a validation group with a

supposedly similar composition of subjects, samples, and/or fragments as the

control group. For example, if the analytics system selected healthy subjects

without cancer for the control group, then the analytics system also uses healthy

subjects without cancer in the validation group.

[00198] The analytics system takes the validation group and generates 100 a set

of methylation state vectors as described in FIG. 2. The analytics system performs

a p-value calculation for each methylation state vector from the validation group.

The p-value calculation process will be further described in conjunction with FIGS.

4 & 5. For each possible methylation state vector, the analytics system calculates

320 a probability from the control group's data structure. Once the probabilities are

calculated for the possibilities of methylation state vectors, the analytics system

calculates 330 a p-value score for that methylation state vector based on the

calculated probabilities. The p-value score represents an expectedness of finding

that specific methylation state vector and other possible methylation state vectors

having even lower probabilities in the control group. A low p-value score, thereby,

generally generallycorresponds correspondsto a tomethylation state state a methylation vector vector which iswhich relatively unexpected unexpected is relatively

in comparison to other methylation state vectors within the control group, whereas

a high p-value score generally corresponds to a methylation state vector which is

relatively more expected in comparison to other methylation state vectors found in

the control group. Once the analytics system generates a p-value score for the

methylation state vectors in the validation group, the analytics system builds 340 a

cumulative density function (CDF) with the p-value scores from the validation

group. The analytics system validates 370 consistency of the CDF as described

above in the fourth type of validation tests.

6.4.2. Anomalously methylated fragments

[00199] Anomalously methylated fragments having abnormal methylation

patterns are selected as target genomic regions, according to an embodiment as

outlined in FIG. 4. An exemplary process of selected anomalously methylated fragments 440 is visually illustrated in FIG. 5, and is further described below the description of FIG. 4. In process 400, the analytics system generates 100 methylation state vectors from cfDNA fragments of the sample. The analytics system handles each methylation state vector as follows.

[00200] In some embodiments, the analytics system filters 405 fragments

having indeterminate states at one or more CpG sites. In such embodiments, the

analytics system implements a prediction model to identify fragments not likely to

have an anomalous methylation pattern for filtering. For a sample fragment, the

prediction model calculates a sample probability that the sample fragment's

methylation state vector occurs in comparison to the healthy control group data

structure. The prediction model randomly samples a subset of possible methylation

state vectors encompassing the CpG sites in the sample fragment's methylation

state vector. The prediction model calculates a probability corresponding to each of

the sampled possible methylation state vectors. Probability calculations for the

fragment's methylation state vector and the sampled possible methylation state

vectors can be calculated according to a Markov chain model as will be described

below in Section 6.4.2.1 ("P-Value Score Calculation"). The prediction model

calculates a proportion of the sampled possible methylation state vectors

corresponding to probabilities less than or equal to the sample probability. The

prediction model generates an estimated p-value score for the fragment based on

the calculated proportion. The prediction model may filter fragments

corresponding to p-value scores above a threshold and retain fragments

corresponding toto corresponding p-value scores p-value belowbelow scores the threshold. Again, see the threshold. e.g.,see Again, U.S. Appl.U.S. Appl. e.g.,

No. 16/352,602, published as U.S. Publ. No. 2019/0287652, which is hereby

incorporated by reference in its entirety.

[00201] In additional embodiments, the prediction model may calculate a

confidence probability that is used by the prediction model to determine when to

continue or when to terminate sampling. The confidence probability describes how

likely it is that a fragment's true p-value score is below a threshold based on the

estimated p-value score and the probabilities of the sampled possible methylation

state vectors. The prediction model may sample one or more possible additional

methylation state vectors while iteratively calculating the estimated p-value score and the confidence probability. The prediction model may then terminate sampling when the confidence probability is above a confidence threshold.

[00202] For a given methylation state vector, the analytics system enumerates

410 all possible methylation state vectors having the same starting CpG site and

same length (i.e., set of CpG sites) as the methylation state vector. There are only

two possible states at each CpG site, methylated or unmethylated, and thus the

count of distinct possibilities of methylation state vectors depends on a power of 2,

such that a methylation state vector of length n would be associated with 2n

possible of methylation state vectors. With methylation state vectors inclusive of

indeterminate states for one or more CpG sites, the analytics system may

enumerate 410 possibilities of methylation state vectors considering only CpG sites

that have observed states.

[00203] The analytics system calculates 420 the probability of observing each

possible methylation state vector for the identified starting CpG site / methylation

state vector length by accessing the healthy control group data structure. In one

embodiment, calculating the probability of observing a given possibility uses

Markov chain probability to model the joint probability calculation which will be

described in greater detail with respect to FIG. 5. In other embodiments,

calculation methods other than Markov chain probabilities are used to determine

the probability of observing each possible methylation state vector.

[00204] The analytics system calculates 430 a p-value score for the methylation

state vector using the calculated probabilities for each possibility. In one

embodiment, this includes determining the calculated probability corresponding to

the possibility that matches the methylation state vector in question. Specifically,

this is the probability of having the same set of CpG sites, or the same starting CpG

site, length and methylation status as the methylation state vector. The analytics

system sums the calculated probabilities of any possibilities having probabilities

less than or equal to the identified probability to generate the p-value score.

[00205] This p-value represents the probability of observing the methylation

state vector of the fragment or other methylation state vectors even less probable in

the the healthy healthycontrol group. control A low group. A p-value score,score, low p-value thereby, generallygenerally thereby, corresponds to a corresponds to a

methylation state vector which is rare in a healthy subject, and which causes the fragment to be labeled anomalously methylated, relative to the healthy control group. A high p-value score generally relates to a methylation state vector is expected to be present, in a relative sense, in a healthy subject. If the healthy control group is a non-cancerous group, for example, a low p-value indicates that the fragment is anamolously methylated relative to the non-cancer group, and therefore possibly indicative of the presence of cancer in the test subject.

[00206] As above, the analytics system calculates p-value scores for each of a

plurality of methylation state vectors, each representing a cfDNA and/or ctDNA

fragment in the test sample. To identify which of the fragments are anomalously

methylated, the analytics system may filter 440 the set of methylation state vectors

based on their p-value scores. In one embodiment, filtering is performed by

comparing the p-values scores against a threshold and keeping only those

fragments below the threshold. This threshold p-value score could be on the order

of 0.1, 0.01, 0.001, 0.0001, or similar.

6.4.2.1. P-value score calculation

[00207] FIG. 5 is an illustration 500 of an example p-value score calculation,

according to an embodiment. To calculate a p-value score given a test methylation

state vector 505, the analytics system takes that test methylation state vector 505

and enumerates 410 possibilities of methylation state vectors. In this illustrative

example, the test methylation state vector 505 is < M23, M24, M25, U26 >. As the

length of the test methylation state vector 505 is 4, there are 2^4 possibilities of

methylation state vectors encompassing CpG sites 23 - 26. In a generic example,

the number of possibilities of methylation state vectors is 2^n, where n is the length

of the test methylation state vector or alternatively the length of the sliding window

(described further below).

[00208] The analytics system calculates 420 probabilities 515 for the

enumerated possible methylation state vectors. As methylation is conditionally

dependent on methylation state of nearby CpG sites, one way to calculate the

probability of observing a given methylation state vector possibility is to use a

Markov chain model. Generally, a methylation state vector such as <S1, S2, <S, S, ,

Sn>, where S denotes the methylation state whether methylated (denoted as M), unmethylated (denoted as U), or indeterminate (denoted as I), has a joint probability that can be expanded using the chain rule of probabilities as:

P(< S1, S2, , Sn >) = P(Sn S1, , Sn-1) * P(Sn-1 | S1, , Sn-2 ) * * P(S2|S1) * P(S1). P(<S,S, S >) = P(S| S, Sn-1 * S, S) * *P(S|S)* P(S).

A Markov

[00209] A Markov chain chain model model canused can be be used to calculate to calculate the conditional the conditional

probabilities of each possibility more efficiently. In one embodiment, the analytics

system selects a Markov chain order k which corresponds to how many prior CpG

sites in the vector (or window) to consider in the conditional probability

calculation, such that the conditional probability is modeled as P(Sn S1, S, ,Sn-1 S ) ) ~

P(Sn|Sn-k-2, P(S| S, ,Sn-1). S).

[00210] To calculate each Markov modeled probability for a possibility of

methylation state vector, the analytics system accesses the control group's data

structure, specifically the counts of various strings of CpG sites and states. To

calculate calculateP(Mn P(M |ISn-k-2, Sn-k-2,..., S),Sn-1), the analytics the analytics system system takesaa ratio takes ratio of of the the stored stored

count count of ofthe thenumber of of number strings from from strings the data the structure matchingmatching data structure < Sn-k-2, <..., S, ,Sn-1, S,

Mn M >> divided divided by by the the sum sum of of the the stored stored count count of of the the number number of of strings strings from from the the data data

structure structurematching matching< Sn-k-2, , Sn-1, < Sn-k-2, S, MMn> >and and << Sn-k-2, Sn-k-2, Sn-1, , S, UUn >. >. Thus, Thus, P(Mn P(Mn |

Sn-k-2, S-k-2, ..., Sn-1), S), is is calculated calculated ratioratio having having the the form: form:

# # of<Sn-k-2,...,Sn-1,Mn > # of < Sn-k-2,...,Sn-1,Mn>+# of <Sn-k-2...,Sn-1,Un> <

[00211] The calculation may additionally implement a smoothing of the counts

by applying a prior distribution. In one embodiment, the prior distribution is a

uniform prior as in Laplace smoothing. As an example of this, a constant is added

to the numerator and another constant (e.g., twice the constant in the numerator) is

added to the denominator of the above equation. In other embodiments, an

algorithmic technique such as Knesser-Ney smoothing is used.

[00212] In the illustration, the above denoted formulas are applied to the test

methylation state vector 505 covering sites 23 - 26. Once the calculated

probabilities 515 are completed, the analytics system calculates 430 a p-value score

525 that sums the probabilities that are less than or equal to the probability of

possibility of methylation state vector matching the test methylation state vector

505.

[00213] In embodiments with indeterminate states, the analytics system may

calculate a p-value score summing out CpG sites with indeterminates states in a

fragment's methylation state vector. The analytics system identifies all possibilities

that have consensus with all methylation states of the methylation state vector,

excluding the indeterminate states. The analytics system may assign the probability

to the methylation state vector as a sum of the probabilities of the identified

possibilities. As an example, the analytics system calculates a probability of a

methylation methylationstate vector state of <of vector M1, < I2, U3 >U as M, I, a sum > as of the a sum of probabilities for thefor the the probabilities

possibilities possibilitiesofof methylation statestate methylation vectors of < M1, vectors of M2, U3 M, < M, > and U >< and M1, < U2,M,U3U, > U >

since methylation states for CpG sites 1 and 3 are observed and in consensus with

the fragment's methylation states at CpG sites 1 and 3. This method of summing

out CpG sites with indeterminate states uses calculations of probabilities of

possibilities up to 2^i, wherein i denotes the number of indeterminate states in the

methylation state vector. In additional embodiments, a dynamic programming

algorithm may be implemented to calculate the probability of a methylation state

vector with one or more indeterminate states. Advantageously, the dynamic

programming algorithm operates in linear computational time.

[00214] In one embodiment, the computational burden of calculating

probabilities and/or p-value scores may be further reduced by caching at least some

calculations. For example, the analytic system may cache in transitory or persistent

memory calculations of probabilities for possibilities of methylation state vectors

(or windows thereof). If other fragments have the same CpG sites, caching the

possibility probabilities allows for efficient calculation of p-value scores without

needing to re-calculate the underlying possibility probabilities. Equivalently, the

analytics system may calculate p-value scores for each of the possibilities of

methylation state vectors associated with a set of CpG sites from vector (or

window thereof). The analytics system may cache the p-value scores for use in

determining the p-value scores of other fragments including the same CpG sites.

Generally, the p-value scores of possibilities of methylation state vectors having

the same CpG sites may be used to determine the p-value score of a different one

of the possibilities from the same set of CpG sites.

6.4.2.2. Sliding window

[00215] In one embodiment, the analytics system uses 435 a sliding window to

determine possibilities of methylation state vectors and calculate p-values. Rather

than enumerating possibilities and calculating p-values for entire methylation state

vectors, the analytics system enumerates possibilities and calculates p-values for

only a window of sequential CpG sites, where the window is shorter in length (of

CpG sites) than at least some fragments (otherwise, the window would serve no

purpose). The window length may be static, user determined, dynamic, or

otherwise selected.

[00216] In calculating p-values for a methylation state vector larger than the

window, the window identifies the sequential set of CpG sites from the vector

within the window starting from the first CpG site in the vector. The analytic

system calculates a p-value score for the window including the first CpG site. The

analytics system then "slides" the window to the second CpG site in the vector, and

calculates another p-value score for the second window. Thus, for a window size l /

and methylation vector length m, each methylation state vector will generate m m-

1+11p-value 1+ p-valuescores. scores.After Aftercompleting completingthe thep-value p-valuecalculations calculationsfor foreach eachportion portionof of

the vector, the lowest p-value score from all sliding windows is taken as the overall

p-value score for the methylation state vector. In another embodiment, the analytics

system aggregates the p-value scores for the methylation state vectors to generate

an overall p-value score.

[00217] Using the sliding window helps to reduce the number of enumerated

possibilities of methylation state vectors and their corresponding probability

calculations that would otherwise need to be performed. Example probability

calculations are shown in FIG. 5, but generally the number of possibilities of

methylation state vectors increases exponentially by a factor of 2 with the size of

the methylation state vector. To give a realistic example, it is possible for

fragments to have upwards of 54 CpG sites. Instead of computing probabilities for

2^54 (~1.8x10^16) possibilities to generate a single p-score, the analytics system

can instead use a window of size 5 (for example) which results in 50 p-value

calculations for each of the 50 windows of the methylation state vector for that

fragment. Each of the 50 calculations enumerates 2^5 (32) possibilities of

methylation state vectors, which total results in 50x2^5 (1.6x10^3) probability calculations. This results in a vast reduction of calculations to be performed, with no meaningful hit to the accurate identification of anomalous fragments. This additional step can also be applied when validating 240 the control group with the validation group's methylation state vectors.

6.4.3. Analysis of hyper or hypomethylated fragments

[00218] In some embodiments, an additional filtration step can be performed to

identify genomic regions that can be targeted for detection of cancer, a cancer

tissue of origin, or a type of cancer.

[00219] The analytics system may perform any variety and/or possibility of

additional analyses with the set of anomalously methylated fragments. One

additional analysis identifies 450 hypomethylated fragments or hypermethylated

fragments from the filtered set. Fragments that are hypomethylated or

hypermethylated can be defined as fragments of a certain length of CpG sites (e.g.,

more than 3, 4, 5, 6, 7, 8, 9, 10, etc.) with a high percentage of methylated CpG

range of 50%-100%) or a high percentage of unmethylated CpG sites (e.g., more

than 80%, 85%, 90%, or 95%, or any other percentage within the range of 50% -

100%), respectively. FIG. 6, described below, illustrates an exemplary process for

identifying these anomalously hypermethylated or hypomethylated portions of a

genome based on the set of anomalously methylated fragments.

[00220] An alternate analysis applies 460 a trained classification model on the

set of anomalous fragments. The trained classification model can be trained to

identify any condition of interest that can be identified from the methylation state

vectors. In one embodiment, the trained classification model is a binary classifier

trained based on methylation states for cfDNA fragments obtained from a subject

cohort with cancer, and optionally based on methylation states for cfDNA

fragments obtained from a healthy subject cohort without cancer, and is then used

to classify a test subject probability of having cancer, or not having cancer, based

on anomalously methylation state vectors. In further embodiments, different

classifiers may classifiers be be may trained using trained subject using cohorts subject known to cohorts have to known particular cancer have particular cancer

(e.g., breast, lung, prostrate, etc.) to predict whether a test subject has those specific

cancers.

PCT/US2019/053509

[00221] In one embodiment, the classifier is trained based on information about

hyper/hypo methylated regions from the process 450 and as described with respect

to FIG. 6 below.

[00222] FIG. FIG. 66 is is aa flowchart flowchart describing describing aa process process 600 600 of of training training aa classifier classifier

based on methylation status of cfDNA fragments, according to an embodiment. An

analytics system may be used to perform the process 600. The process accesses

two training groups of samples - a non-cancer group and a cancer group - and

obtains 400 a non-cancer set of methylation state vectors and a cancer set of

methylation state vectors comprising the anomalous fragments of the samples in

each group. The anomalous fragments may be identified according to the process

of FIG. 4, for example.

[00223] The analytics system determines 610, for each methylation state

vector, whether the methylation state vector is hypomethylated or hypermethylated.

Here, the hypermethylated or hypomethylated label is assigned if at least some

number of CpG sites have a particular state (methylated or unmethylated,

respectively) and/or have a threshold percentage of sites that are the particular state

(again, methylated or unmethylated, respectively). In one example, cfDNA

fragments are identified as hypomethylated or hypermethylated, respectively, if the

fragment overlaps at least 5 CpG sites, and at least 80%, 90% or 100% of its CpG

sites are methylated or at least 80%, 90%, or 100% are unmethylated.

[00224] In an alternate embodiment, the analytics system considers portions of

the methylation state vector and determines whether the portion is hypomethylated

or hypermethylated, and may distinguish that portion to be hypomethylated or

hypermethylated. This alternative resolves missing methylation state vectors which

are large in size but contain at least one region of dense hypomethylation or

hypermethylation. This process of defining hypomethylation and hypermethylation

can be applied in step 450 of FIG. 4.

[00225] In one embodiment, the analytics system generates 620 a

hypomethylation score (Phypo) and aa hypermethylation (Phyp) and hypermethylation score score (Phyper) (Phyper) per per CpG CpG site site in in

the genome. To generate either score at a given CpG site, the classifier takes four

counts at that CpG site - (1) count of (methylations state) vectors of the cancer set

labeled labeledhypomethylated hypomethylatedthatthat overlap the CpG overlap thesite; CpG (2) count site; (2)ofcount vectors of of the vectors of the cancer set labeled hypermethylated that overlap the CpG site; (3) count of vectors of the non-cancer set labeled hypomethylated that overlap the CpG site; and (4) count of vectors of the non-cancer set labeled hypermethylated that overlap the

CpG site. Additionally the process may normalize these counts for each group to

account for variance in group size between the non-cancer group and the cancer

group.

[00226] In one embodiment, the hypomethylation score at a given CpG site is

defined as a log of a ratio of (1) over (3). Similarly the hypermethylation score is

calculated as a log of a ratio of (2) over (4). Additionally these ratios may be

calculated with an additional smoothing technique as discussed above.

[00227] In another embodiment, the hypomethylation score is defined as a ratio

of (1) over (1) summed with (3). The hypermethylation score is defined as a ratio

of (2) over (2) summed with (4). Similar to the embodiment above, smoothing

techniques may be implemented into the ratios.

[00228] The analytics system generates 630 an aggregate hypomethylation

score and an aggregate hypermethylation score for each anomalous methylation

state vector. The aggregate hyper and hypo methylation scores, are determined

based on the hyper and hypo methylation scores of the CpG sites in the

methylation state vector. In one embodiment, the aggregate hyper and hypo

methylation scores are assigned as the largest hyper and hypo methylation scores

of the sites in each state vector, respectively. However, in alternate embodiments,

the aggregate scores could be based on means, medians, or other calculations that

use the hyper/hypo methylation scores of the sites in each vector. In one

embodiment, the analytics system assigns the greater of the aggregate

hypomethylation score and the aggregate hypermethylation score to the anomalous

methylation state vector.

[00229] The analytics system then ranks 640 all of that subject's methylation

state vectors by their aggregate hypomethylation score and by their aggregate

hypermethylation score, resulting in two rankings per subject. The process selects

aggregate hypomethylation scores from the hypomethylation ranking and

aggregate hypermethylation scores from the hypermethylation ranking. With the

selected scores, the classifier generates 650 a single feature vector for each subject.

In one embodiment, the scores selected from either ranking are selected with a

fixed order that is the same for each generated feature vector for each subject in

each of the training groups. As an example, in one embodiment the classifier

selects the first, the second, the fourth, the eighth, the sixteenth, the thirty-second,

and the sixty-fourth aggregate hyper methylation score, and similarly for each

aggregate hypo methylation score, from each ranking and writes those scores in the

feature vector for that subject (totaling 14 features in the feature vector). In

additional embodiments, to adjust for sample sequencing depth, the analytics

system adjusts ranks in linear proportion to relative sample depth. For example, if

the relative sample depth was X, interpolated scores were taken at *the x*theoriginal original

ranks (i.e. x=1.1, we take scores computed at ranks 1.1, 2.2, x*2i). The

analytics system may then define the feature vector based on the adjusted ranks to

be used in further classification.

[00230] The analytics system trains 660 a binary classifier to distinguish

feature vectors between the cancer and non-cancer training groups. The analytics

system may group the training samples into sets of one or more training samples

for iterative batch training of the binary classifier. After inputting all sets of

training samples including their training feature vectors and adjusting the

classification parameters, the binary classifier is sufficiently trained to label test

samples samplesaccording accordingto to their feature their vector feature withinwithin vector some margin some of error.of error. margin

[00231] In one embodiment, the classifier is a non-linear classifier. In a specific

embodiment, the classifier is a non-linear classifier utilizing a L2-regularized

kernel logistic regression with a Gaussian radial basis function (RBF) kernel.

Specifically, a regularized kernel logistic regression classifier (KLR) was trained

using the isotropic radial basis function (power exponential 2) as the kernel with

scale parameter gamma and L2 regularization parameter lambda. Gamma and

lambda were optimized for holdout log-loss using internal cross-validation within

specified training data, and were optimized using grid-search in multiplicative

steps, starting at the maximum value and halving the parameter each step. In other

embodiments, the classifier can include other types of classifiers, such as a random

forest classifier, a mixture model, a convolutional neural network, or an

autoencoder model.

[00232] In some embodiments, calculation is performed with respect to each

CpG site. Specifically a first count is determined that is the number of cancerous

samples (cancer_count) that include a fragment overlapping that CpG, and a

second count is determined that is the total number of samples containing

fragments overlapping that CpG (total) in the set. Genomic regions can be selected

based on the numbers, for example, based on criteria positively correlated to the

number of cancerous samples (cancer_count) that include a fragment overlapping

that CpG, and inversely correlated to the total number of samples containing

fragments overlapping fragments overlapping that that CpG CpG (total) (total) in theinset. theSpecifically, set. Specifically, in one embodiment, in one embodiment,

the number of non-cancer samples (nnon-cancer) and the number of cancer samples

(ncancer) having a fragment overlapping a CpG site are counted. Then the probability

that a sample is cancer is estimated as (ncancer + 1) / (ncancer + nnon-cancer + -2). CpG 2). CpG

sites by this metric were ranked and greedily added to a panel until the panel size

budget is exhausted.

[00233] In some cases, additional analysis calculates the log-odds ratio that the

anomalous fragments from a subject are indicative of cancer generally. The log-

odds ratio can be calculated by taking the log of a ratio of a probability of being

cancerous over a probability of being non-cancerous (i.e., one minus the

probability of being cancerous), both as determined by the applied classification

model.

6.4.4. Off-target genomic regions

[00234] In some embodiments, probes targeting selected genomic regions are

further filtered 465 based on the number of their off-target regions. This is for

screening probes that pull down too many cfDNA fragments containing off-target

genomic regions. Exclusion of probes having many off-target regions can be

valuable by decreasing off-target rates and increasing target coverage for a given

amount of sequencing.

[00235] An off-target genomic region is a genomic region that has sufficient

homology to a target genomic region to be bound and pulled down by a probe

designed to target the target genomic region. An off-target genomic region can be

a genomic region (or a converted sequence of that same region) that aligns to a

probe along at least 35bp, 40bp, 45bp, 50bp, 60bp, 70bp, or 80bp with at least an

PCT/US2019/053509

80%, 85%, 90%, 95%, or 97% match rate. In one embodiment, an off-target

genomic region is a genomic region (or a converted sequence of that same region)

that aligns to a probe along at least 45bp with at least a 90% match rate. Various

methods known in the art can be adopted to screen off-target genomic regions.

[00236] Exhaustively searching the genome to find all off-target genomic

regions can be computationally challenging challenging.In Inone oneembodiment, embodiment,a ak-mer k-merseeding seeding

strategy (which can allow one or more mismatches) is combined to local alignment

at the seed locations. In this case, exhaustive searching of good alignments can be

guaranteed based on k-mer length, number of mismatches allowed, and number of

k-mer seed hits at a particular location. This requires doing dynamic programing

local alignment at a large number of locations, SO so this approach is highly optimized

to use vector CPU instructions (e.g., AVX2, AVX512) and also can be parallelized

across many cores within a machine and also across many machines connected by

a network. A person of ordinary skill will recognize that modifications and

variations of this approach can be implemented for the purpose of identifying off-

target genomic regions.

[00237] In some embodiments, probes having off-target genomic regions more

than a threshold number are excluded. For example, probes having more than 30,

more than 25, more than 20, more than 18, more than 15, more than 12, more than

10, or more than 5 are excluded.

[00238] In some embodiments, probes are divided into 2, 3, 4, 5, 6, or more

separate groups depending on the numbers of off-target regions. For example,

probes having no off-target regions are assigned to high-quality group, probes

having 1-19 off-target regions are assigned to low-quality group, and probes

having more than 19 off-target regions are assigned to poor-quality group. Other

cut-off values can be used for the grouping.

[00239] In some In some embodiments, embodiments, probes probes in in the the lowest lowest quality quality group group are are excluded. excluded.

In some embodiments, probes in groups other than the highest-quality group are

excluded. In some embodiments, separate panels are made for the probes in each

group. In some embodiments, all the probes are put on the same panel, but

separate analysis is performed based on the assigned groups.

[00240] In some embodiments, a panel comprises a larger number of high-

quality probes than the number of probes in lower groups. In some embodiments,

a panel comprises a smaller number of poor-quality probes than the number of

probes in other group. In some embodiments, more than 95%, 90%, 85%, 80%,

75%, or 70% of probes in a panel are high-quality probes. In some embodiments,

less than 35%, 30%, 20%, 10%, 5%, 4%, 3%, 2% or 1% of the probes in a panel

are low-quality probes. In some embodiments, less than 5%, 4%, 3%, 2% or 1% of

the probes in a panel are poor-quality probes. In some embodiments, no poor-

quality probes are included in a panel.

[00241] In some embodiments, probes having below 50%, below 40%, below

30%, below 20%, below 10% or below 5% are excluded. In some embodiments,

probes having above 30%, above 40%, above 50%, above 60%, above 70%, above

80%, or above 90% are selectively included in a panel.

6.5. Methods of using cancer assay panel

[00242] In yet another aspect, methods of using a cancer assay panel are

provided. The methods can comprise steps of bisulfite treatment cfDNA fragments

to convertunmethylated to convert unmethylated cytosines cytosines to uracils, to uracils, applying applying the to the samples samples to the cancer the cancer

assay panel and sequencing cfDNA fragments that bind to the probes in the panel.

The sequence reads can be further compared to a reference genome. This assay

can allow identification of methylation states at CpG sites within the fragments and

thus provide information relevant to diagnosis of cancer.

6.5.1. Sample processing

[00243] FIG. 7A is a flowchart of a method for preparing a nucleic acid sample

for analysis according to one embodiment. The method includes, but is not limited

to, the following steps. For example, any step of the method may comprise a

quantitation sub-step for quality control or other laboratory assay procedures

known to one skilled in the art.

[00244] In step 105, a nucleic acid sample (DNA or RNA) is extracted from a

subject. In the present disclosure, DNA and RNA may be used interchangeably

unless otherwise indicated. That is, the embodiments described herein may be

applicable to both DNA and RNA types of nucleic acid sequences. However, the examples described herein may focus on DNA for purposes of clarity and explanation. The sample may be any subset of the human genome, including the whole genome. The sample may include blood, plasma, serum, urine, fecal, saliva, other types of bodily fluids, or any combination thereof. In some embodiments, methods for drawing a blood sample (e.g., syringe or finger prick) may be less invasive than procedures for obtaining a tissue biopsy, which may require surgery.

The extracted sample may comprise cfDNA and/or ctDNA. For healthy

individuals, the human body may naturally clear out cfDNA and other cellular

debris. If a subject has a cancer or disease, cfDNA or ctDNA in an extracted

sample may be present at a detectable level for diagnosis.

[00245] In step 110, the cfDNA fragments are treated to convert unmethylated

cytosines to uracils. In one embodiment, the method uses a bisulfite treatment of

the DNA which converts the unmethylated cytosines to uracils without converting

the methylated cytosines. For example, a commercial kit such as the EZ DNA

Methylation Gold, EZ DNA TM - Gold, EZMethylationTM Direct DNA Methylation or an or - Direct EZ an DNA EZ DNA

MethylationTM Methylation TM- -Lightning Lightningkit kit(available (availablefrom fromZymo ZymoResearch ResearchCorp Corp(Irvine, (Irvine,CA)) CA))

is used for the bisulfite conversion. In another embodiment, the conversion of

unmethylated cytosines to uracils is accomplished using an enzymatic reaction. For

example, the conversion can use a commercially available kit for conversion of

unmethylated cytosines to uracils, such as APOBEC-Seq (NEBiolabs, Ipswich,

MA).

[00246] In step 115, a sequencing library is prepared. In a first step, a ssDNA

adapter is added to the 3'-OH end of a bisulfite-converted ssDNA molecule using a

ssDNA ligation reaction. In one embodiment, the ssDNA ligation reaction uses

CircLigase II (Epicentre) to ligate the ssDNA adapter to the 3'-OH end of a

bisulfite-converted ssDNA molecule, wherein the 5'-end of the adapter is

phosphorylated and the bisulfite-converted ssDNA has been dephosphorylated (i.e.,

the 3' end has a hydroxyl group). In another embodiment, the ssDNA ligation

reaction uses Thermostable 5' AppDNA/RNA ligase (available from New England

BioLabs (Ipswich, MA)) to ligate the ssDNA adapter to the 3'-OH end of a

bisulfite-converted ssDNA molecule. In this example, the first UMI adapter is

adenylated at the 5'-end and blocked at the 3'-end. In another embodiment, the

ssDNA ligation reaction uses a T4 RNA ligase (available from New England

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

BioLabs) to ligate the ssDNA adapter to the 3'-OH end of a bisulfite-converted

ssDNA molecule. In a second step, a second strand DNA is synthesized in an

extension reaction. For example, an extension primer, that hybridizes to a primer

sequence included in the ssDNA adapter, is used in a primer extension reaction to

form a double-stranded bisulfite-converted DNA molecule. Optionally, in one

embodiment, the extension reaction uses an enzyme that is able to read through

uracil residues in the bisulfite-converted template strand. Optionally, in a third

step, a dsDNA adapter is added to the double-stranded bisulfite-converted DNA

molecule. Finally, the double-stranded bisulfite-converted DNA is amplified to add

sequencing adapters. For example, PCR amplification using a forward primer that

includes a P5 sequence and a reverse primer that includes a P7 sequence is used to

add P5 and P7 sequences to the bisulfite-converted DNA. Optionally, during

library preparation, unique molecular identifiers (UMI) may be added to the

nucleic acid molecules (e.g., DNA molecules) through adapter ligation. The UMIs

are short nucleic acid sequences (e.g., 4-10 base pairs) that are added to ends of

DNA fragments during adapter ligation. In some embodiments, UMIs are

degenerate base pairs that serve as a unique tag that can be used to identify

sequence reads originating from a specific DNA fragment. During PCR

amplification following adapter ligation, the UMIs are replicated along with the

attached DNA fragment, which provides a way to identify sequence reads that

came from the same original fragment in downstream analysis.

[00247] In step 120, targeted DNA sequences may be enriched from the library.

This is used, for example, where a targeted panel assay is being performed on the

samples. During enrichment, hybridization probes (also referred to herein as

"probes") are used to target, and pull down, nucleic acid fragments informative for

the presence or absence of cancer (or disease), or cancer status. For a given

workflow, the probes may be designed to anneal (or hybridize) to a target

(complementary) strand of DNA or RNA. The target strand may be the "positive"

strand (e.g., the strand transcribed into mRNA, and subsequently translated into a

protein) or the complementary "negative" strand. The probes may range in length

from 10s, 100s, or 1000s of base pairs. Moreover, the probes may cover

overlapping portions of a target region.

73

[00248] After a hybridization step 120, the hybridized nucleic acid fragments

are captured and may also be amplified using PCR (enrichment 125). For example,

the target sequences can be enriched to obtain enriched sequences that can be

subsequently sequenced. In general, any known method in the art can be used to

isolate, and enrich for, probe-hybridized target nucleic acids. For example, as is

well known in the art, a biotin moiety can be added to the 5'-end of the probes (i.e.,

biotinylated) to facilitate isolation of target nucleic acids hybridized to probes

using a streptavidin-coated surface (e.g., streptavidin-coated beads).

[00249] In step 130, sequence reads are generated from the enriched DNA

sequences, e.g., enriched sequences. Sequencing data may be acquired from the

enriched DNA sequences by known means in the art. For example, the method

may include next generation sequencing (NGS) techniques including synthesis

technology (Illumina), pyrosequencing (454 Life Sciences), ion semiconductor

technology (Ion Torrent sequencing), single-molecule real-time sequencing

(Pacific Biosciences), sequencing by ligation (SOLiD sequencing), nanopore

sequencing (Oxford Nanopore Technologies), or paired-end sequencing. In some

embodiments, massively parallel sequencing is performed using sequencing-by-

synthesis with reversible dye terminators.

6.5.2. Analysis of sequence reads

[00250] In some embodiments, the sequence reads may be aligned to a

reference genome using known methods in the art to determine alignment position

information. The alignment position information may indicate a beginning

position and an end position of a region in the reference genome that corresponds

to a beginning nucleotide base and end nucleotide base of a given sequence read.

Alignment position information may also include sequence read length, which can

be determined from the beginning position and end position. A region in the

reference genome may be associated with a gene or a segment of a gene.

[00251] In various embodiments, a sequence read is comprised of a read pair

denoted as R1 andR. R and R2. For For example, example, the the first first read read R R1 maymay be be sequenced sequenced from from a a

first end of a nucleic acid fragment whereas the second read R2 maybe R may besequenced sequenced

from the second end of the nucleic acid fragment. Therefore, nucleotide base pairs

of of the the first firstread R1 Rand read second and readread second R2 may be aligned R may consistently be aligned (e.g., in consistently (e.g., in opposite orientations) with nucleotide bases of the reference genome. Alignment position information derived from the read pair R1 and RR2 R and may may include include a a beginning beginning position in the reference genome that corresponds to an end of a first read (e.g., R1) R) and an end position in the reference genome that corresponds to an end of a second read (e.g., R2). Inother R). In otherwords, words,the thebeginning beginningposition positionand andend endposition positionin inthe the reference genome represent the likely location within the reference genome to which the nucleic acid fragment corresponds. In one embodiment, the read pair R1 R and R2 can be R can be assembled assembled into into aa fragment, fragment, and and the the fragment fragment used used for for subsequent subsequent analysis and/or classification. An output file having SAM (sequence alignment map) format or BAM (binary alignment map) format may be generated and output for further analysis.

[00252] From the sequence reads, the location and methylation state for each of

CpG site may be determined based on alignment to a reference genome. Further, a

methylation state vector for each fragment may be generated specifying a location

of the fragment in the reference genome (e.g., as specified by the position of the

first CpG site in each fragment, or another similar metric), a number of CpG sites

in the fragment, and the methylation state of each CpG site in the fragment whether

methylated (e.g., denoted as M), unmethylated (e.g., denoted as U), or

indeterminate (e.g., denoted as I). The methylation state vectors may be stored in

temporary or persistent computer memory for later use and processing. Further,

duplicate reads or duplicate methylation state vectors from a single subject may be

removed. In an additional embodiment, it may be determined that a certain

fragment has one or more CpG sites that have an indeterminate methylation status.

Such fragments may be excluded from later processing or selectively included

where downstream data model accounts for such indeterminate methylation

statuses.

[00253] FIG. 7B is an illustration of the process 100 of FIG. 7A of sequencing

a cfDNA fragment to obtain a methylation state vector, according to an

embodiment. As an example, the analytics system takes a cfDNA fragment 112. In

this example, the cfDNA fragment 112 contains three CpG sites. As shown, the

first and third CpG sites of the cfDNA fragment 112 are methylated 114. During

the treatment step 120, the cfDNA fragment 112 is converted to generate a

converted cfDNA fragment 122. During the treatment 120, the second CpG site

WO wo 2020/069350 PCT/US2019/053509

which was unmethylated has its cytosine converted to uracil. However, the first

and third CpG sites are not converted.

[00254] After conversion, a sequencing library 130 is prepared and sequenced

140 generating a sequence read 142. The analytics system aligns 150 the sequence

read 142 to a reference genome 144. The reference genome 144 provides the

context as to what position in a human genome the fragment cfDNA originates

from. In this simplified example, the analytics system aligns 150 the sequence read

such that the three CpG sites correlate to CpG sites 23, 24, and 25 (arbitrary

reference identifiers used for convenience of description). The analytics system

thus generates information both on methylation status of all CpG sites on the

cfDNA fragment 112 and which to position in the human genome the CpG sites

map. As shown, the CpG sites on sequence read 142 which were methylated are

read as cytosines. In this example, the cytosines appear in the sequence read 142

only in the first and third CpG site which allows one to infer that the first and third

CpG sites in the original cfDNA fragment were methylated. Whereas, the second

CpG site is read as a thymine (U is converted to T during the sequencing process),

and thus, one can infer that the second CpG site was unmethylated in the original

cfDNA fragment. With these two pieces of information, the methylation status and

location, the analytics system generates 160 a methylation state vector 152 for the

fragment cfDNA 112. In this example, the resulting methylation state vector 152 is

< < M23, M, U,U24, M25wherein M >, >, wherein M correspondstotoa amethylated M corresponds methylated CpG CpG site, site,U U

corresponds to an unmethylated CpG site, and the subscript number corresponds to

a position of each CpG site in the reference genome.

[00255] FIGS. 9A & 9B show three graphs of data validating the consistency of

sequencing from a control group. The first graph 170 shows conversion accuracy

of conversion of unmethylated cytosines to uracil (step 120) on cfDNA fragment

obtained from a test sample across subjects in varying stages of cancer - stage I,

stage II, stage III, stage IV, and non-cancer. As shown, there was uniform

consistency in converting unmethylated cytosines on cfDNA fragments into

uracils. There was an overall conversion accuracy of 99.47% with a precision at ±

0.024%. The second graph 180 shows mean coverage over varying stages of

cancer. The mean coverage over all groups being ~ 34x mean across the genome

coverage of DNA fragments, using only those confidently mapped to the genome are counted. The third graph 190 shows concentration of cfDNA per sample across varying stages of cancer.

6.5.3. Detection of cancer

[00256] Sequence reads or fragments obtained by the methods provided herein

can be analyzed by a medical professional, or further processed by an automated

algorithms. For example, the analytics system is used to receive sequencing data

from a sequencer and perform various aspects of processing as described herein.

The analytics system can be one of a personal computer (PC), a desktop computer,

a laptop computer, a notebook, a tablet PC, a mobile device. A computing device

can be communicatively coupled to the sequencer through a wireless, wired, or a

combination of wireless and wired communication technologies. Generally, the

computing device is configured with a processor and memory storing computer

instructions that, when executed by the processor, cause the processor to perform

steps as described in the remainder of this document. Generally, the amount of

genetic data and data derived therefrom is sufficiently large, and the amount of

computational power required SO so great, SO so as to be impossible to be performed on

paper or by the human mind alone.

[00257] The clinical interpretation of methylation status of targeted genomic

regions is a process that includes classifying the clinical effect of each or a

combination of the methylation status and reporting the results in ways that are

meaningful to a medical professional. The clinical interpretation can be based on

comparison of the sequence reads with a database specific to cancer or non-cancer

subjects, and/or based on numbers and types of the cfDNA fragments having

cancer-specific methylation patterns identified from a sample. In some

embodiments, targeted genomic regions are ranked or classified based on their

likeness to be differentially methylated in cancer samples, and the ranks or

classifications are used in the interpretation process. The ranks and classifications

can include (1) the type of clinical effect, (2) the strength of evidence of the effect,

and (3) the size of the effect. Various methods for clinical analysis and

interpretation of genome data can be adopted for analysis of the sequence reads. In

some other embodiments, the clinical interpretation of the methylation states of

such differentially methylated regions can be based on machine learning

PCT/US2019/053509

approaches that interpret a current sample based on a classification or regression

method that was trained using the methylation states of such differentially

methylated regions from samples from cancer and non-cancer patients with known

cancer status, cancer type, cancer stage, tissue of origin, etc.

[00258] The clinically meaningful information can include the presence or

absence of cancer generally, presence or absence of certain types of cancers, cancer

stage, or presence or absence of other types of diseases. In some embodiments, the

information relates to a presence or absence of one or more cancer types, selected

from the group consisting of anorectal cancer, bladder and urothelial cancer, blood

cancer, breast cancer (hormone receptor positive and hormone receptor negative),

biliary tract cancer, cervical cancer, colorectal cancer, endometrial cancer,

esophageal cancer, head and neck cancer, gastric cancer, hepatobiliary cancer, liver

cancer, lung cancer, lymphoid neoplasm, gall bladder cancer, melanoma, multiple

myeloma, ovarian cancer, pancreatic cancer, upper GI tract cancer, prostate cancer,

renal cancer, sarcoma, thyroid cancer, bile duct cancer, urothelial cancer, and

uterine cancer.

Cancer classifier

[00259] To train a cancer type classifier, the analytics system obtains a plurality

of training samples each having a set of hypomethylated and hypermethylated

fragments indicative of cancer, e.g., identified via step 450 in the process 400, and

a label of the training sample's cancer type. The analytics system determines, for

each training sample, a feature vector based on the set of hypomethylated and

hypermethylated fragments indicative of cancer. The analytics system calculates an

anomaly score for each CpG site in the targeted genomic regions. In one

embodiment, the analytics system defines the anomaly score for the feature vector

as a binary scoring based on whether there is a hypomethylated or hypermethylated

fragment from the set that encompasses the CpG site. Once all anomaly scores are

determined for a training sample, the analytics system determines the feature

vector as a vector of elements including, for each element, one of the anomaly

scores associated with one of the CpG sites. The analytics system may normalize

the anomaly scores of the feature vector based on a coverage of the sample, i.e., a

median or average sequencing depth over all CpG sites.

[00260] With the feature vectors of the training samples, the analytics system

can train the cancer classifier. In one embodiment, the analytics system trains a

binary cancer classifier to distinguish between the labels, cancer and non-cancer,

based on the feature vectors of the training samples. In this embodiment, the

classifier outputs a prediction score indicating the likelihood of the presence or

absence of cancer. In another embodiment, the analytics system trains a multiclass

cancer classifier to distinguish between many cancer types (e.g., between head and

neck cancer, liver/bileduct cancer, upper GI cancer, pancreatic/gallbladder cancer;

colorectal cancer, ovarian cancer, lung cancer, multiple myeloma, lymphoid

neoplasms, melanoma, sarcoma, breast cancer, and uterine cancer). In this

multiclass cancer classifier embodiment, the cancer classifier is trained to

determine a cancer prediction that comprises a prediction value for each of the

cancer types being classified for. The prediction values may correspond to a

likelihood that a given sample has each of the cancer types. For example, the

cancer classifier returns a cancer prediction including a prediction value for breast

cancer, lung cancer, and non-cancer. For example, the cancer classifier may return

a cancer prediction for a test sample including a prediction score for breast cancer,

lung cancer, and/or no cancer. In either embodiment, the analytics system trains the

cancer classifier by inputting sets of training samples with their feature vectors into

the cancer classifier and adjusting classification parameters SO so that a function of the

classifier accurately relates the training feature vectors to their corresponding label.

The analytics system may group the training samples into sets of one or more

training samples for iterative batch training of the cancer classifier. After inputting

all sets of training samples including their training feature vectors and adjusting the

classification parameters, the cancer classifier is sufficiently trained to label test

samples according to their feature vector within some margin of error. The

analytics system may train the cancer classifier according to any one of a number

of methods. As an example, the binary cancer classifier may be a L2-regularized

logistic regression classifier that is trained using a log-loss function. As another

example, the multi-cancer classifier may be a multinomial logistic regression. In

practice either type of cancer classifier may be trained using other techniques.

These techniques are numerous including potential use of kernel methods, machine

learning algorithms such as multilayer neural networks, etc. In particular, methods

as described in PCT/US2019/022122 and U.S. Patent. App. No. 16/352,602 which

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

are incorporated by reference in their entireties herein can be used for various

embodiments.

[00261] In particular embodiments, a cancer classifier is trained by the process

comprising the steps of: a. obtaining sequence information of training fragments

from a plurality of training subjects; b. for each training fragment, determining

whether that training fragment is hypomethylated or hypermethylated, wherein

each of the hypomethylated and hypermethylated training fragments comprises at

CpG sites being unmethylated or methylated, respectively, C. c. for each training

subject, generating a training feature vector based on the hypomethylated training

fragments and hypermethylated training fragments, and d. training the model with

the training feature vectors from the one or more training subjects without cancer

and the training feature vectors from the one or more training subjects with cancer.

The training method can further comprise the steps of: a. obtaining sequence

information of training fragments from a plurality of training subjects; b. for each

hypermethylated, wherein each of the hypomethylated and hypermethylated

training fragments comprises at least a threshold number of CpG sites with at least

a threshold percentage of the CpG sites being unmethylated or methylated,

respectively, C. c. for each of a plurality of CpG sites in a reference genome:

quantifying a count of hypomethylated training fragments which overlap the CpG

site and a count of hypermethylated training fragments which overlap the CpG site;

and generating a hypomethylation score and a hypermethylation score based on the

count of hypomethylated training fragments and hypermethylated training

fragments; d. for each training fragment, generating an aggregate hypomethylation

score based on the hypomethylation score of the CpG sites in the training fragment

and an aggregate hypermethylation score based on the hypermethylation score of

the CpG sites in the training fragment; e. for each training subject: ranking the

plurality of training fragments based on aggregate hypomethylation score and

ranking the plurality of training fragments based on aggregate hypermethylation

score; and generating a feature vector based on the ranking of the training

fragments; f. obtaining training feature vectors for one or more training subjects

without cancer and training feature vectors for the one or more training subjects with cancer; and g. training the model with the feature vectors for the one or more training subjects without cancer and the feature vectors for the one or more training subjects with cancer. In some embodiments, the model comprises one of a kernel logistic regression classifier, a random forest classifier, a mixture model, a convolutional neural network, and an autoencoder model.

[00262] In some embodiments, quantifying a count of hypomethylated training

fragments which overlap that CpG site and a count of hypermethylated training

fragments which overlap that CpG site further comprises: a. quantifying a cancer

count of hypomethylated training fragments from the one or more training subjects

with cancer that overlap that CpG site and a non-cancer count of hypomethylated

training fragments from the one or more training subjects without cancer that

overlap that CpG site; and b. quantifying a cancer count of hypermethylated

training fragments from the one or more training subjects with cancer that overlap

that CpG site and a non-cancer count of hypermethylated training fragments from

the one or more training subjects without cancer that overlap that CpG site. In

some embodiments, generating a hypomethylation score and a hypermethylation

score based on the count of hypomethylated training fragments and

hypermethylated training fragments further comprises: a. for generating the

hypomethylation score, calculating a hypomethylation ratio of the cancer count of

hypomethylated training fragments over a hypomethylation sum of the cancer

count of hypomethylated training fragments and the non-cancer count of

hypomethylated training fragments; and b. for generating the hypermethylation

score, calculating a hypermethylation ratio of the cancer count of hypermethylated

training fragments over a hypermethylation sum of the cancer count of

hypermethylated training fragments and the non-cancer count of hypermethylated

training fragments.

[00263] During deployment, the analytics system obtains sequence reads from

a test sample collected from a subject. Various sequencing methods available in the

art can be used to obtain sequence reads. In some embodiments, the sequence reads

are obtained from whole genome sequencing or targeted sequencing. In some

embodiments, the sequence reads include a set of sequence reads of modified test

fragments, wherein the modified test fragments are obtained by processing a set of

nucleic acid fragments, wherein each of the nucleic acid fragments corresponds to or is derived from a plurality of genomic regions selected from any one of Lists 1-

8. In some embodiments, the sequence reads are from the DNA samples enriched

using the assay panel described herein.

[00264] The analytics system processes the sequence reads to obtain a test

feature vector in a similar process as described for the training samples. In some

embodiments, the test feature vector is obtained by the process comprising a. for

each of the nucleic acid fragments, determining whether the nucleic acid fragment

is hypomethylated or hypermethylated, wherein each of the hypomethylated and

hypermethylated nucleic acid fragments comprises at least a threshold number of

or methylated, respectively; b. for each of a plurality of CpG sites in a reference

genome: quantifying a count of hypomethylated nucleic acid fragments which

overlap the CpG site; and generating a hypomethylation score and a

hypermethylation score based on the count of hypomethylated nucleic acid

fragments and hypermethylated nucleic acid fragments; C. c. for each nucleic acid

fragment, generating an aggregate hypomethylation score based on the

hypomethylation score of the CpG sites in the nucleic acid fragment and an

aggregate hypermethylation score based on the hypermethylation score of the CpG

sites in the nucleic acid fragment; d. ranking the plurality of nucleic acid fragments

based on aggregate hypomethylation score and ranking the plurality of nucleic

fragments based on aggregate hypermethylation score; and e. generating the test

feature vector based on the ranking of the nucleic acid fragments.

[00265] The analytics system then inputs the test feature vector into the trained

cancer classifier to yield a cancer prediction, e.g., binary prediction (cancer or non-

cancer) or multiclass cancer prediction (prediction score for each of a plurality of

cancer types). In some embodiments, the analytics system outputs a cancer

probability for the test sample. The cancer probability can be compared to a

cancer or without cancer.

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

Exemplary sequencer and analytics system

[00266] FIG. 8A is a flowchart of systems and devices for sequencing nucleic

acid samples according to one embodiment. This illustrative flowchart includes

devices such as a sequencer 820 and an analytics system 800. The sequencer 820

and the analytics system 800 may work in tandem to perform one or more steps in

the processes described herein.

[00267] In various embodiments, the sequencer 820 receives an enriched

nucleic acid sample 810. As shown in FIG. 8A, the sequencer 820 can include a

graphical user interface 825 that enables user interactions with particular tasks

(e.g., initiate sequencing or terminate sequencing) as well as one more loading

stations 830 for loading a sequencing cartridge including the enriched fragment

samples and/or for loading necessary buffers for performing the sequencing assays.

Therefore, once a user of the sequencer 820 has provided the necessary reagents

and sequencing cartridge to the loading station 830 of the sequencer 820, the user

can initiate sequencing by interacting with the graphical user interface 825 of the

sequencer 820. Once initiated, the sequencer 820 performs the sequencing and

outputs the sequence reads of the enriched fragments from the nucleic acid sample

810. 810.

[00268] In some embodiments, the sequencer 820 is communicatively coupled

with the analytics system 800. The analytics system 800 includes some number of

computing devices used for processing the sequence reads for various applications

such as assessing methylation status at one or more CpG sites, variant calling or

quality control. The sequencer 820 may provide the sequence reads in a BAM file

format to the analytics system 800. The analytics system 800 can be

communicatively coupled to the sequencer 820 through a wireless, wired, or a

combination of wireless and wired communication technologies. Generally, the

analytics system 800 is configured with a processor and non-transitory computer-

readable storage medium storing computer instructions that, when executed by the

processor, cause the processor to process the sequence reads or to perform one or

more steps of any of the methods or processes disclosed herein.

[00269] In some embodiments, the sequence reads may be aligned to a

reference genome using known methods in the art to determine alignment position information, e.g., information, e.g., part part of step of step 140the 140 of ofprocess the process 100 in 100 in FIG. 3A. FIG. 3A. Alignment Alignment position may generally describe a beginning position and an end position of a region in the reference genome that corresponds to a beginning nucleotide based and an end nucleotide base of a given sequence read. Corresponding to methylation sequencing, the alignment position information may be generalized to indicate a first CpG site and a last CpG site included in the sequence read according to the alignment to the reference genome. The alignment position information may further indicate methylation statuses and locations of all CpG sites in a given sequence read. A region in the reference genome may be associated with a gene or a segment of a gene; as such, the analytics system 800 may label a sequence read with one or more genes that align to the sequence read. In one embodiment, fragment length (or size) is determined from the beginning and end positions.

In various

[00270] In various embodiments, embodiments, for example for example whenwhen a paired-end a paired-end sequencing sequencing

process is used, a sequence read is comprised of a read pair denoted as R_1 and

R 2. For example, the first read R R_2. 1 may be sequenced from a first end of a R_1

R 2 may be double-stranded DNA (dsDNA) molecule whereas the second read R_2

sequenced from the second end of the double-stranded DNA (dsDNA). Therefore,

nucleotide base pairs of the first read R 1 and second read R R_1 2 may be aligned R_2

consistently (e.g., in opposite orientations) with nucleotide bases of the reference

genome. Alignment position information derived from the read pair R 1 and R R_1 2 R_2

may include a beginning position in the reference genome that corresponds to an

end of a first read (e.g., R_1) and an end position in the reference genome that

corresponds to an end of a second read (e.g., R_2). In other words, the beginning

position and end position in the reference genome represent the likely location

within the reference genome to which the nucleic acid fragment corresponds. In

one embodiment, the read pair R1 and RR2 R and can can bebe assembled assembled into into a a fragment, fragment, and and

the fragment used for subsequent analysis and/or classification. An output file

having SAM (sequence alignment map) format or BAM (binary) format may be

generated and output for further analysis.

Referring

[00271] Referring nowFIG. now to to FIG. 8B, FIG. 8B, FIG. 8Bais 8B is a block block diagram diagram ofanalytics of an an analytics

system 800 for processing DNA samples according to one embodiment. The

analytics system implements one or more computing devices for use in analyzing

DNA samples. The analytics system 800 includes a sequence processor 840,

PCT/US2019/053509

sequence database 845, model database 855, models 850, parameter database 865,

and score engine 860. In some embodiments, the analytics system 800 performs

one or more steps in the processes 100 of FIG. 3A, 340 of FIG. 3B, 400 of FIG. 4,

500 of FIG. 5, 600 of FIG. 6A, or 680 of FIG. 6B and other process described

herein.

[00272] The sequence processor 840 generates methylation state vectors for

fragments from a sample. At each CpG site on a fragment, the sequence processor

840 generates a methylation state vector for each fragment specifying a location of

the fragment in the reference genome, a number of CpG sites in the fragment, and

the methylation state of each CpG site in the fragment whether methylated,

unmethylated, or indeterminate via the process 100 of FIG. 3A. The sequence

processor 840 may store methylation state vectors for fragments in the sequence

database 845. Data in the sequence database 845 may be organized such that the

methylation state vectors from a sample are associated to one another.

[00273] Further, multiple different models 850 may be stored in the model

database 855 or retrieved for use with test samples. In one example, a model is a

trained cancer classifier for determining a cancer prediction for a test sample using

a feature vector derived from anomalous fragments. The training and use of the

cancer classifier is discussed elsewhere herein. The analytics system 800 may train

the one or more models 850 and store various trained parameters in the parameter

database 865. The analytics system 800 stores the models 850 along with

functions in the model database 855.

[00274] During inference, the score engine 860 uses the one or more models

850 to return outputs. The score engine 860 accesses the models 850 in the model

database 855 along with trained parameters from the parameter database 865.

According to each model, the score engine receives an appropriate input for the

model and calculates an output based on the received input, the parameters, and a

function of each model relating the input and the output. In some use cases, the

score engine 860 further calculates metrics correlating to a confidence in the

calculated outputs from the model. In other use cases, the score engine 860

calculates other intermediary values for use in the model.

Application

[00275] In some embodiments, the methods, analytic systems and/or classifier

of the present invention can be used to detect the presence (or absence) of cancer,

monitor cancer progression or recurrence, monitor therapeutic response or

effectiveness, determine a presence or monitor minimum residual disease (MRD),

or any combination thereof. In some embodiments, the analytic systems and/or

classifier may be used to identify the tissue or origin for a cancer. For instance, the

systems and/or classifiers may be used to identify a cancer as of any of the

following cancer types: head and neck cancer, liver/bileduct cancer, upper GI

cancer, pancreatic/gallbladder cancer; colorectal cancer, ovarian cancer, lung

cancer, multiple myeloma, lymphoid neoplasms, melanoma, sarcoma, breast

cancer, and uterine cancer. For example, as described herein, a classifier can be

used to generate a likelihood or probability score (e.g., from 0 to 100) that a sample

feature vector is from a subject with cancer. In some embodiments, the probability

score is compared to a threshold probability to determine whether or not the subject

has cancer. In other embodiments, the likelihood or probability score can be

assessed at different time points (e.g., before or after treatment) to monitor disease

progression or to monitor treatment effectiveness (e.g., therapeutic efficacy). In

still other embodiments, the likelihood or probability score can be used to make or

influence a clinical decision (e.g., diagnosis of cancer, treatment selection,

assessment of treatment effectiveness, etc.). For example, in one embodiment, if

the likelihood or probability score exceeds a threshold, a physician can prescribe an

appropriate treatment.

Early detection of cancer

[00276] In some embodiments, the methods and/or classifier of the present

invention are used to detect the presence or absence of cancer in a subject

suspected of having cancer. For example, a classifier (as described herein) can be

used to determine a likelihood or probability score that a sample feature vector is

from a subject that has cancer.

[00277] In one embodiment, a probability score of greater than or equal to 60

can indicated that the subject has cancer. In still other embodiments, a probability

score greater than or equal to 65, greater than or equal to 70, greater than or equal to 75, greater than or equal to 80, greater than or equal to 85, greater than or equal to 90, or greater than or equal to 95, indicated that the subject has cancer. In other embodiments, a probability score can indicate the severity of disease. For example, a probability score of 80 may indicate a more severe form, or later stage, of cancer compared to a score below 80 (e.g., a score of 70). Similarly, an increase in the probability score over time (e.g., at a second, later time point) can indicate disease progression or a decrease in the probability score over time (e.g., at a second, later time point) can indicate successful treatment.

[00278] In another embodiment, a cancer log-odds ratio can be calculated for a

test subject by taking the log of a ratio of a probability of being cancerous over a

probability of being non-cancerous (i.e., one minus the probability of being

cancerous), as described herein. In accordance with this embodiment, a cancer log-

odds ratio greater than 1 can indicate that the subject has cancer. In still other

embodiments, a cancer log-odds ratio greater than 1.2, greater than 1.3, greater

than 1.4, greater than 1.5, greater than 1.7, greater than 2, greater than 2.5, greater

than 3, greater than 3.5, or greater than 4, indicated that the subject has cancer. In

other embodiments, a cancer log-odds ratio can indicate the severity of disease.

For example, a cancer log-odds ratio greater than 2 may indicate a more severe

form, or later stage, of cancer compared to a score below 2 (e.g., a score of 1).

Similarly, an increase in the cancer log-odds ratio over time (e.g., at a second, later

time point) can indicate disease progression or a decrease in the cancer log-odds

ratio over time (e.g., at a second, later time point) can indicate successful

treatment.

[00279] According to aspects of the invention, the methods and systems of the

present invention can be trained to detect or classify multiple cancer indications.

For example, the methods, systems and classifiers of the present invention can be

used to detect the presence of one or more, two or more, three or more, five or

more, or ten or more different types of cancer.

[00280] In some embodiments, the cancer is one or more of head and neck

cancer, liver/bile duct cancer, upper GI cancer, pancreatic/gallbladder cancer;

colorectal cancer, ovarian cancer, lung cancer, multiple myeloma, lymphoid

neoplasms, melanoma, sarcoma, breast cancer, and uterine cancer. In some embodiments, the cancer is one or more of anorectal cancer, bladder or urothelial cancer, or cervical cancer.

Cancer and Treatment Monitoring

[00281] In some embodiments, the likelihood or probability score can be

assessed at different time points (e.g., or before or after treatment) to monitor

disease progression or to monitor treatment effectiveness (e.g., therapeutic

efficacy). For example, the present disclosure provides methods that involve

obtaining a first sample (e.g., a first plasma cfDNA sample) from a cancer patient

at a first time point, determining a first likelihood or probability score therefrom

(as described herein), obtaining a second test sample (e.g., a second plasma cfDNA

sample) from the cancer patient at a second time point, and determine a second

likelihood or probability score therefrom (as described herein).

[00282] In certain embodiments, the first time point is before a cancer

treatment (e.g., before a resection surgery or a therapeutic intervention), and the

second time point is after a cancer treatment (e.g., after a resection surgery or

therapeutic intervention), and the method utilized to monitor the effectiveness of

the treatment. For example, if the second likelihood or probability score decreases

compared to the first likelihood or probability score, then the treatment is

considered to have been successful. However, if the second likelihood or

probability score increases compared to the first likelihood or probability score,

then the treatment is considered to have not been successful. In other embodiments,

both the first and second time points are before a cancer treatment (e.g., before a

resection surgery or a therapeutic intervention). In still other embodiments, both

the first and the second time points are after a cancer treatment (e.g., before a

resection surgery or a therapeutic intervention) and the method is used to monitor

the effectiveness of the treatment or loss of effectiveness of the treatment. In still

other embodiments, cfDNA samples may be obtained from a cancer patient at a

first and second time point and analyzed. e.g., to monitor cancer progression, to

determine if a cancer is in remission (e.g., after treatment), to monitor or detect

residual disease or recurrence of disease, or to monitor treatment (e.g., therapeutic)

efficacy.

[00283] Those of skill in the art will readily appreciate that test samples can be

obtained from a cancer patient over any desired set of time points and analyzed in

accordance with the methods of the invention to monitor a cancer state in the

patient. In some embodiments, the first and second time points are separated by an

amount of time that ranges from about 15 minutes up to about 30 years, such as

about 30 minutes, such as about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

17, 18, 19, 20, 21, 22, 23, or about 24 hours, such as about 1, 2, 3, 4, 5, 10, 15, 20,

25 or about 30 days, or such as about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months,

or such as about 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10,

10.5, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5, 15, 15.5, 16, 16.5, 17, 17.5, 18, 18.5,

19, 19.5, 20, 20.5, 21, 21.5, 22, 22.5, 23, 23.5, 24, 24.5, 25, 25.5, 26, 26.5, 27,

27.5, 28, 28.5, 29, 29.5 or about 30 years. In other embodiments, test samples can

be obtained from the patient at least once every 3 months, at least once every 6

months, at least once a year, at least once every 2 years, at least once every 3 years,

at least once every 4 years, or at least once every 5 years.

Treatment

[00284] In still another embodiment, information obtained from any method

described herein (e.g., the likelihood or probability score) can be used to make or

influence a clinical decision (e.g., diagnosis of cancer, treatment selection,

appropriate treatment (e.g., a resection surgery, radiation therapy, chemotherapy,

and/or immunotherapy). In some embodiments, information such as a likelihood or

probability score can be provided as a readout to a physician or subject.

[00285] A classifier (as described herein) can be used to determine a likelihood

or probability score that a sample feature vector is from a subject that has cancer.

In one embodiment, an appropriate treatment (e.g., resection surgery or

therapeutic) is prescribed when the likelihood or probability exceeds a threshold.

For example, in one embodiment, if the likelihood or probability score is greater

than or equal to 60, one or more appropriate treatments are prescribed. In another

embodiments, embodiments, if if the the likelihood likelihood or or probability probability score score is is greater greater than than or or equal equal to to 65, 65,

greater than or equal to 70, greater than or equal to 75, greater than or equal to 80,

PCT/US2019/053509

greater than or equal to 85, greater than or equal to 90, or greater than or equal to

95, one or more appropriate treatments are prescribed. In other embodiments, a

cancer log-odds ratio can indicate the effectiveness of a cancer treatment. For

example, an increase in the cancer log-odds ratio over time (e.g., at a second, after

treatment) can indicate that the treatment was not effective. Similarly, a decrease

in the cancer log-odds ratio over time (e.g., at a second, after treatment) can

indicate successful treatment. In another embodiment, if the cancer log-odds ratio

is greater than 1, greater than 1.5, greater than 2, greater than 2.5, greater than 3,

greater than 3.5, or greater than 4, one or more appropriate treatments are

prescribed.

[00286] In some embodiments, the treatment is one or more cancer therapeutic

agents selected from the group consisting of a chemotherapy agent, a targeted

cancer cancer therapy therapyagent, a differentiating agent, therapy a differentiating agent, agent, therapy a hormone a therapy hormoneagent, and agent, and therapy

an immunotherapy agent. For example, the treatment can be one or more

chemotherapy agents selected from the group consisting of alkylating agents,

(taxans), topoisomerase inhibitors, mitotic inhibitors, corticosteroids, kinase

inhibitors, nucleotide analogs, platinum-based agents and any combination thereof.

In some embodiments, the treatment is one or more targeted cancer therapy agents

selected from the group consisting of signal transduction inhibitors (e.g. tyrosine

kinase and growth factor receptor inhibitors), histone deacetylase (HDAC)

inhibitors, retinoic receptor agonists, proteosome inhibitors, angiogenesis

inhibitors, and monoclonal antibody conjugates. In some embodiments, the

treatment is one or more differentiating therapy agents including retinoids, such as

tretinoin, alitretinoin and bexarotene. In some embodiments, the treatment is one or

more hormone therapy agents selected from the group consisting of anti-estrogens,

aromatase inhibitors, progestins, estrogens, anti-androgens, and GnRH agonists or

analogs. In one embodiment, the treatment is one or more immunotherapy agents

selected from the group comprising monoclonal antibody therapies such as

rituximab (RITUXAN) and alemtuzumab (CAMPATH), non-specific

immunotherapies and adjuvants, such as BCG, interleukin-2 (IL-2), and interferon-

alfa, immunomodulating drugs, for instance, thalidomide and lenalidomide

(REVLIMID). It is within the capabilities of a skilled physician or oncologist to select an appropriate cancer therapeutic agent based on characteristics such as the type of tumor, cancer stage, previous exposure to cancer treatment or therapeutic agent, and other characteristics of the cancer.

6.6. Examples

[00287] The following examples are put forth SO so as to provide those of ordinary

skill in the art with a complete disclosure and description of how to make and use

the present description, and are not intended to limit the scope of what the

inventors regard as their description nor are they intended to represent that the

experiments below are all or the only experiments performed. Efforts have been

made to ensure accuracy with respect to numbers used (e.g., amounts, temperature,

etc.) but some experimental errors and deviations should be accounted for.

6.6.1. Example 1: Sequencing cfDNA from individuals with cancer and non-cancer controls

[00288] The Circulating Cell-free Genome Atlas Study ("CCGA"; Clinical

Trial.gov identifier NCT02889978) is a prospective, multi-center, case-control,

observational study with longitudinal follow-up. De-identified biospecimens were

collected from approximately 15,000 participants from 142 sites. Samples were

selected to ensure a prespecified distribution of cancer types and non-cancers

across sites in each cohort, and cancer and non-cancer samples were frequency

age-matched by gender. The STRIVE study is a prospective, multi-center,

observational cohort study designed to validate an assay for the early detection of

breast cancer and other invasive cancers (See Clinical Trail.gov Identifier:

NCT03085888 (clinicaltrials.gov/ct2/show/NCT03085888)). Asdescribed (clinicaltrials.gov/ct2/show/NCT03085888). As describedbelow, below,

additional non-cancer samples were selected from the STRIVE study and used for

classifier training.

[00289] In some embodiments, whole-genome bisulfite sequencing (WGBS;

30x depth) of cfDNA isolated from plasma from the CCGA study subjects was

employed for analysis of cfDNA. In other embodiments, as indicated below, a

targeted bisulfite sequencing procedure was used for analysis of sample. Under the

targeted bisulfite sequencing procedure, a probe set was used to enrich cfDNA

molecules derived from a plurality of target genomic regions. For both processes, cfDNA was extracted from two tubes of plasma (up to a combined volume of 10 ml) per patient using a modified QIAamp Circulating Nucleic Acid kit (Qiagen;

Germantown, MD). Up to 75 ng of plasma cfDNA was subjected to bisulfite

conversion using the EZ-96 DNA Methylation Kit (Zymo Research, D5003).

Converted cfDNA was used to prepare dual indexed sequencing libraries using

Accel-NGS Methyl-Seq DNA library preparation kits (Swift BioSciences; Ann

Arbor, MI). The constructed libraries were quantified using the KAPA Library

Quantification Kit for Illumina Platforms (Kapa Biosystems; Wilmington, MA).

Four libraries along with 10% PhiX v3 library (Illumina, FC-110-3001) were

pooled and clustered on an Illumina NovaSeq 6000 S2 flow cell followed by 150-

bp paired-end sequencing (30x).

6.6.2. Example 2: Modeling the methylation status of healthy individuals

[00290] A statistical model and a data structure of typical cfDNA fragments

were produced using an independent reference set of 108 non-smoking participants

without cancer (age: 5814 58±14years, years,79 79[73%]

[73%]women) women)(i.e., (i.e.,aareference referencegenome) genome)

from the CCGA study. These samples were used to train a Markov-chain model

(order 3) estimating the likelihood of a given sequence of CpG methylation

statuses statuseswithin withina fragment as further a fragment described as further above in described Section above in 6.4.2.1 Section1 6.4.2.1-P-Value - -Value

Score Calculation. This model was demonstrated to be calibrated within the normal

fragment range (p-value>0.001) and was used to reject fragments with a p-value

from the Markov model of >0.001 as insufficiently 0.001 as insufficiently unusual. unusual.

6.6.3. Example 3: Selection of target genomic regions

[00291] Target genomic regions were selected using a database generated by

sequencing bisulfite converted cfDNA fragments obtained from 1548 individuals

who had been diagnosed with cancer and 1163 individuals who had not been

diagnosed with cancer from the CCGA study.

[00292] The process for selecting target genomic regions began by identifying

anomalous cfDNA fragments from among the bisulfite converted cfDNA fragment

sequences from each individual. The fragments were aligned to the hg19 reference

genome, and each CpG site within each fragment was scored as methylated or unmethylated, or indeterminate. Anomalous cfDNA fragments have three characteristics: (1) a bisulfite converted methylation pattern with a p-value of <

0.001 according to the Markov-chain model, indicating that the methylation pattern

would not be expected to occur in individuals without cancer; (2) five or more CpG

sites; and (3) a hypermethylated or hypomethylated state wherein at least 80%,

90%, or 100% of the CpG sites are either methylated or unmethylated,

respectively. Indeterminant sites were included in the total number of CpG sites

when calculating the percentage of methylated or unmethylated CpG sites.

[00293] The second step in target selection was to determine a ranking score

for each of the approximately 30 million CpG sites in the GRCh37/hg19 reference

genome. For each CpG site, a count was made of cancer and non-cancer samples

having at least one anomalous cfDNA fragment overlapping the CpG site.

Hypermethylated anomalous fragments and hypermethylated anomalous fragments

were counted separately. These counts were used to separately calculate scores for

hypermethylated and hypomethylated fragments according to the following

formula: (ncancer + 1) / (ncancer + nnon-cancer + 2). Three pairs of scores were calculated

for each CpG: a first pair for anomalous fragments having an 80% or more

hypermethylation or hypomethylation state; a second pair for anomalous fragments

having a 90% or more hypermethylation or hypomethylation state; and a third pair

for anomalous fragments having a 100% hypermethylation or hypomethylation

state. The highest of these six scores became the ranking score for the CpG site.

[00294] The third step in target selection was to define targeted regions within

the genome. The first region added to the target list was a 30-base region extending

15 bases on either side of the center of the highest ranking CpG site. The list of

target regions was then expanded in an iterative process. A 30-base region

extending 15 bases on either side of the center of the next highest ranking CpG site

was identified. If the new region was within 200 bases of an existing target site, the

new region was merged with the existing one to form a larger region including the

all bases between the new 30-base region and the old one. If the new region was

not within 200 bases of an existing target site, it was simply added to the list of

target regions. The list of target regions was iteratively expanded until it reached

the desired panel size of 0.59 MB for Assay Panel 1, 1.19 MB for Assay Panel 2,

PCT/US2019/053509

2.38 MB for Assay Panel 3, 4.96 MB for Assay Panel 4, and 8.53 MB for Assay

Panel 5. Panel 5.

6.6.4. Example 4: Probe design

[00295] Biotinylated probes were designed for the purpose of collecting

bisulfite-converted anomalous cfDNA fragments that are derived from the genomic

regions identified above. Each probe was 120 bases long and had a biotin moiety at

its 5' end. Generally, probe sets were designed to include probes that target

fragments from each of the CpG sites included within the start/stop ranges of any

of the targeted regions included in any one of Lists 1-8.

[00296] To increase the probability of capturing anomalous cfDNA fragments

derived from a target region (or a portion of a target region), including anomalous

cfDNA fragments with partial target sequences at their 5' or 3' ends, probes were

arranged in a tiled fashion SO so that two probes aligned to each base within a target

region. Adjacent 120-base probes were designed to overlap by 60 bases. Target

regions of 60 or fewer bases were initially targeted by three probes as illustrated in

FIG. FIG. 1A. 1A. Additional Additional probes probes were were used used for for target target regions regions of of larger larger size size (see (see e.g., e.g.,

FIG. 1B). For both size classes of target regions, the probes used to interrogate

each region collectively extend beyond the target region (e.g., to include at least 60

bases of non-targeted sequence on either side of the target region due to the tiled

arrangement; see FIG. 1A). Additionally, probes were designed to enrich DNA

molecules derived from both strands of DNA from each target region, such that a

single basepair single base pair of of a target a target genomic genomic regionregion may be may be targeted targeted by fourtwo by four probes: probes: for two for

each strand.

[00297] Probe sequences were designed to be complementary to bisulfite-

converted anomalous cfDNA fragments from each of the genomic regions, where

every CpG site in a cfDNA fragment is either methylated or unmethylated. Thus, if

the anomalous cfDNA fragments aligning with a target sequence are

hypomethylated, all, or most of the CpG sites within the fragment would be

expected to be converted to UpG sequences, and the corresponding probes would

have CpA sequences for all, or most of the CpGs in the target. Conversely, if the

anomalous cfDNA sequences aligning with a target sequence are hypermethylated,

all, or most of the CpG sites within the fragment would be expected to be protected

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

from conversion to UpG sequences, and the corresponding probes would have CpG

sequences for all, or most of the CpGs in the target. Cytosines not located within a

CpG site are generally not methylated. These bystander cytosines are therefore

converted to uracil regardless of the methylation status of adjacent CpG sites.

Probes matching a target site were designed to pull down hypomethylated cfDNA

fragments or hypermethylated cfDNA fragments, but generally not both. This

"semibinary" design contrasts with a "binary" design where cfDNA fragments

from the same target region are targeted with two sets of probes, one designed to

pull down hypomethylated cfDNA and the other designed to pull down

hypermethylated cfDNA at the same region.

6.6.5. Example 5: Probe quality control

[00298] Poor quality probes likely to pull down a significant number off-target

cfDNA fragments were excluded from the cancer assay panels.

[00299] To determine the characteristics of a poor-quality probe, an experiment

was conducted to test how much overlap between a cfDNA fragment and a probe is

required to achieve a non-negligible amount of pulldown.

[00300] Purified, bisulfite converted cfDNA was mixed with biotinylated

probes in Hybridization Capture Buffer (Argonaut Technologies, Redwood City,

CA, Cat# 310450) supplemented with 1 mM Tris, pH 8.0 in a 96-well plate, and

processed in a Hamilton Star Liquid Handler (Hamilton, Reno, NV, Cat# STAR

AL 8/96 iSWAP) and a 96-well BioRad C1000 Touch Thermocycler (BiRad,

Hercules, CA, Cat # 1851196). The temperature of the hybridization mix was

raised to 95°C for 10 minutes, and then gradually decreased by 2°C/min to 62°C,

with 1-minute pauses at 93°C and 91°C. Hybridization continued for 15.5-17.5

hours at 62°C. A second aliquot of probes was added, and the thermal cycling

program was repeated. Captured cfDNA was captured on Streptavidin Magnetic

Beads (Illumina, San Diego, CA, Cat# 20014367), amplified by PCR, and then

sequenced.

[00301] Various overlap lengths were tested using preliminary assay panels

(V1D3, V1D4, V1E2) designed to include probes having various overlaps ranging

from 0 to 120 bases. Samples comprising 175 bp target DNA fragments were

PCT/US2019/053509

applied to the panel and washed, and then DNA fragments bound to the probes

were collected. The amounts of the collected DNA fragments were measured, and

then plotted as densities over the sizes of overlaps as provided in FIG. 10.

[00302] There was no significant binding and pull down of target DNA

fragments when the overlap was less than 45 bp. These results indicate that, with

the specified hybridization conditions, a fragment-probe overlap of at least 45 bp is

generally required to maximize hybridization between the probe set and nucleic

acids derived from the target genomic regions, and thus improve pulldown

efficiency.

[00303] Furthermore, binding, and thus pulldown efficiency, is greatly

disrupted when the percentage of mismatches between the probe and fragment

sequences in the region of overlap is greater than 10%. It was therefore concluded

that off-target pulldown may occur for probes having a sequence with a match rate

of at least 90% to an off-target genomic region of at least 45 bp.

[00304] For each probe, we performed an exhaustive, bisulfite aware search of

the entire reference genome for off-target regions having 45 bp alignments with a

90%+ match rate. Specifically, we combined a k-mer seeding strategy (which can

allow one or more mismatches) with local alignment at the seed locations. This

guaranteed against missing any good alignments based on k-mer length, number of

mismatches allowed, and number of k-mer seed hits at a particular location. The

search involved performing dynamic programing local alignment at a large number

of locations, SO so the implementation was optimized to use vector CPU instructions

(e.g., AVX2, AVX512) and parallelized across many cores within a machine and

also across many machines connected by a network. This implementation allows

for an exhaustive search, which is valuable in designing a high-performance panel

(i.e., low off-target rate and high target coverage for a given amount of

sequencing).

[00305] Following the exhaustive search, each probe was scored based on its

number of off-target regions. The best probes (high) have a score of 1, meaning

they match only one genomic region - the target. Probes with an intermediate score

of between 2-19 hits (intermediate) were accepted but probes with a poor score of

20 or more hits (poor) were discarded. Table 1 presents a summary of the probes

WO wo 2020/069350 PCT/US2019/053509

in Assay Panels 3-5, including the numbers and percentages of probes retained

after eliminating low quality probes with potential off-target effects. The probe

region is larger than the target region because some probes extend beyond the ends

of target regions.

Table 1. Probe numbers and target region sizes before and after probe quality

control

Original Final Final probe Final target Fraction of

probes probes target (MB) region (Mb) original target

Assay Panel 3 127,734 102,013 3.798 2.12 88.1%

Assay Panel 4 256,988 189,155 7.013 4.15 83.5%

Assay Panel 5 449,088 303,262 11.35 6.71 78.5%

[00306] Additionally, numbers of high quality, intermediate quality, and poor-

quality probes were counted among probes targeting hypermethylated genomic

regions or hypomethylated genomic regions. As shown in FIG. 11, probes targeting

hypermethylated genomic regions tend to have significantly less off-target

matches.

6.6.6. Example 4: Characterization of target genomic regions

[00307] Lists 1 to 8 (see Table 2 below) present Assay Panels of various sizes

representing the portion of targeted genomic regions for which probes were

designed to enrich DNA molecules derived from these targeted genomic regions.

Assay Panels 1-5 are arranged from small to large according to the size budget

used to select their target genomic regions. These Assay Panels were filtered to

exclude certain CpG sites and genomic regions. Assay Panel 6, which defines a

larger genomic region than any of Assay Panels 1-5, includes all of the CpG sites

and genomic regions that were filtered out to produce each of Assay Panels 1-5.

Assay Panels 3A and 4A present subsets of the target genomic regions in Assay

Panels 3 and 4, respectively, excluding genomic regions that are also described in

PCT/US2019/025358.

97

Table 2. Correspondence between Assay Panels, Lists, and SEQ ID NOs

List SEQ ID NO range Name Assay Panel 1 List 1 1 1644 Assay Panel 2 List 2 1645 5270 Assay Panel 3 List 3 5271 - 16837 5271 16837 Assay Panel 3A List 4 16838 - 25984 Assay Panel 4 List 5 25985 - 46929 Assay Panel 4A List 6 46930 - 67335 Assay Panel 5 List 7 67336 - 101617 Assay Panel 6 List 8 101618 - 131822

[00308] The sequence listing includes the following information: (1) SEQ ID

NO, (2) a sequence identifier that identifies (a) a chromosome or contig on which

the CpG site is located and (b) a start and stop position of the region, (3) the

sequence corresponding to (2) and (4) whether the region was included based on its

hypermethylation or hypomethylation score. The chromosome numbers and the

start and stop positions are provided relative to a known human reference genome,

GRCh37/hg19 GRCh37/hg19.The Thesequence sequenceof ofGRCh37/hg19 GRCh37/hg19is isavailable availablefrom fromthe theNational National

Center for Biotechnology Information (NCBI), the Genome Reference Consortium,

and the Genome Browser provided by Santa Cruz Genomics Institute.

[00309] Generally, Generally, a aprobe probe cancan be designed be designed to target to target any of any the of CpG the CpG sites sites

included within the start/stop ranges of any of the targeted regions included in Lists

1-8. Alternatively stated, in some embodiments, probes can be designed to

hybridize to any CpG site in fragments derived from any of the targeted regions,

such as converted fragments from the target region.

[00310] The Assay Panels were analyzed to understand their features. The sizes

of target genomic regions of the Assay Panels, prior to filtering out off-target

probes, are shown in Table 10. The number of CpG sites per target genomic region,

for each of various assay panels (prior to filtering out off-target probes), is plotted

in FIG. 12A as a function of density. The per probe G/C content of various assay

panels is presented in FIG. 12.B.

Table 10. Target genomic region sizes

Region Assay Assay Assay Assay Assay size Panel 1 Panel 2 Panel 3 Panel 4 Panel 6 Smallest 15 15 15 15 15

Median - - 95 94 82 Mean - - 207 207 229 220 Largest 2593 3374 3374 4368 6820

[00311] Target genomic regions within the Assay Panels were next aligned to a

reference genome to evaluate their locations. Target genomic regions were

positioned in introns, exons, intergenic regions, 5'UTRs, 3'UTRs, or controlling

regions such as promoters or enhancers. The number of target genomic regions

that fall within each genomic annotation for Assay Panels 3-5 were counted and

plotted in the graphs provided in FIG. 13. FIG. 13 also compares numbers of the

selected target genomic regions (black bars) with numbers of randomly selected

genomic regions (gray bars) that fall within each genomic annotation.

[00312] The analysis shows that the selected target genomic regions are not

random in their genomic distributions. Functional elements such as promoters,

5'UTRs, exons, and intron/exon boundaries were more highly represented in the

assay panels compared to randomly selected regions, while intergenic regions were

underrepresented.

6.6.7. Example 7: Generation of a mixture model classifier

[00313] To maximize performance, the predictive cancer models described in

this Example were trained using sequence data obtained from a plurality of

samples from known cancer types and non-cancers from both CCGA sub-studies

(CCGA1 and CCGA22), a plurality of tissue samples for known cancers obtained

from CCGA1, and a plurality of non-cancer samples from the STRIVE study (See

Clinical Trail.gov Identifier: NCT03085888

(/clinicaltrials.gov/ct2/show/NCT03085888)).The (//clinicaltrials.gov/ct2/show/NCT03085888) TheSTRIVE STRIVEstudy studyisisa aprospective, prospective,

multi-center, observational cohort study to validate an assay for the early detection

of breast cancer and other invasive cancers, from which additional non-cancer

training samples were obtained to train the classifier described herein. The known

cancer types included from the CCGA sample set included the following: breast,

lung, prostate, colorectal, renal, uterine, pancreas, esophageal, lymphoma, head and

neck, ovarian, hepatobiliary, melanoma, cervical, multiple myeloma, leukemia,

thyroid, bladder, gastric, and anorectal. As such, a model can be a multi-cancer

WO wo 2020/069350 PCT/US2019/053509 PCT/US2019/053509

model (or a multi-cancer classifier) for detecting one or more, two or more, three

or more, four or more, five or more, ten or more, or 20 or more different types of

cancer.

[00314] Classifier performance data shown below for Assay Panels 1-6, was

reported out for a locked classifier trained on cancer and non-cancer samples

obtained from the CCGA sub-study (CCGA2) and the STRIVE study. The

individuals in the CCGA2 sub-study were different from the individuals in the

CCGA1 sub-study whose cfDNA was used to select target genomes. From the

CCGA2 study, blood samples were collected from individuals diagnosed with

untreated untreatedcancer (including cancer 20 tumor (including types types 20 tumor and alland stages all of cancer) stages of and healthy cancer) and healthy

individuals with no cancer diagnosis (controls). For STRIVE, blood samples were

collected from women within 28 days of their screening mammogram. Cell-free

DNA (cfDNA) was extracted from each sample and treated with bisulfite to

convert unmethylated cytosines to uracils. The bisulfite treated cfDNA was

enriched for informative cfDNA molecules using hybridization probes designed to

enrich bisulfite-converted nucleic acids derived from each of a plurality of targeted

genomic regions in an assay panel comprising all of the genomic regions of Assay

Panels 1-6. The enriched bisulfite-converted nucleic acid molecules were

sequenced using paired-end sequencing on an Illumina platform (San Diego, CA)

to obtain a set of sequence reads for each of the training samples, and the resulting

read pairs were aligned to the reference genome, assembled into fragments, and

methylated and unmethylated CpG sites identified.

[00315] Classifier performance was evaluated for Assay Panels 1-6, and

reported in this Example and in FIGs. 14-27, over a set of training samples that

included 3,053 samples (1,532 cancer samples and 1,521 non-cancer samples). All

sample types used for evaluating classifier performance are shown below in Table

3. 3.

Table 3. Cancer diagnoses of individuals whose cfDNA was used to train the

classifier

Cancer Type Total Stage I II II III III IV Not Reported Non-cancer 1521 - - -- - -

Lung 261 60 23 72 106 0

Breast 247 102 110 27 27 8 0 Prostate 188 39 113 19 17 0 Lymphoid neoplasm 147 15 27 27 27 39 39 Colorectal 121 13 22 41 45 0 Pancreas and gallbladder 95 15 15 19 46 0 Uterine 84 73 3 5 3 0 Upper GI 67 9 12 12 19 27 0 Head and neck 62 7 13 16 26 0 Renal 56 37 4 4 11 11 0 Ovary 37 4 2 25 6 0 Multiple myeloma 34 10 13 11 0 0 Not reported 29 8 5 7 6 3 Liver bile duct 29 5 7 7 10 0 Sarcoma 17 2 4 5 6 0 Bladder and urothelial 16 3 1 6 7 0 Anorectal 14 4 5 5 0 0 Cervical 11 1 8 2 0 0 1 Melanoma 7 7 3 0 3 0 Myeloid neoplasm 1 1 4 2 0 0 Thyroid 4 0 0 0 0 4 Prediction only 2 0 0 0 2 0

Mixture model based featurization

[00316] For each cancer type (including non-cancer) a probabilistic mixture

model was trained and utilized to assign a probability to each fragment from each

cancer and non-cancer sample based on how likely it was that the fragment would

be observed in a given sample type.

[00317] Briefly, for each sample type (cancer and non-cancer samples), for

each region (where each region was 1 kb in length with a 50% overlap between

adjacent regions), a probabilistic model was fit to the fragments derived from the

training samples for each type or cancer and non-cancer. The probabilistic model

trained for each sample type was a mixture model, where each of three mixture

components was an independent-sites model in which methylation at each CpG is

assumed to be independent of methylation at other CpGs. Fragments were

excluded from the model if: they had a p-value greater than 0.01, were marked as

duplicate fragments, the fragments had a bag size of greater than 1; they did not

cover at least one CpG site; or if the fragment was greater than 1000 bases in

length.

[00318] Each probabilistic model was fit using maximum-likelihood estimation

to identify a set of parameters that maximized the log-likelihood of all fragments

deriving from each sample type, subject to a regularization penalty.

Featurization

[00319] Features were extracted for each fragment from each training sample,

for each cancer type and non-cancer sample, in each region. The extracted features

were the tallies of outlier fragments (i.e., anomalously methylated fragments),

which were defined as those whose log-likelihood under a first cancer model

exceeded the log-likelihood under a second cancer model or non-cancer model by

at least a threshold tier value. Outlier fragments were tallied separately for each

genomic region, sample model (i.e., cancer type), and tier (for tiers 1, 2, 3, 4, 5, 6,

7, 8, and 9), yielding 9 features per region for each sample type.

Feature ranking and classifier training

[00320] For each set of pairwise features, the features were ranked using

mutual information based on their ability to distinguish the first cancer type (which

defined the log-likelihood model from which the feature was derived) from the

second cancer type or non-cancer. The top ranked 256 features from each pairwise

comparison were identified and added to the final feature set for each cancer type

and non-cancer. The features in the final feature set for each sample (cancer type

and non-cancer) were binarized (any feature value greater than 0 was set to 1, SO so

that all features were either 0 or 1). The training samples were then divided into

distinct 5-fold cross-validation training sets and used to train a logistic regression

classifier (for detecting the presence of cancer) and a multiclass logistic regression

classifier (for determining cancer tissue of origin). During the classifier training

process, the cross-validated scores assigned to the training set were collected.

[00321] Once the binary cancer / non-cancer classifier was trained, a

probability score was determined from the classifier for each of the training

samples. Training samples with a probability score that exceeded a threshold were

called as positive for cancer. This threshold was prespecified to target a desired

specificity level, based on the distribution of cross-validated scores assigned to the training set. For example, if the desired specificity level was 99.4%, the threshold would be set to the 99.4th percentile of the cross-validated cancer detection probability scores assigned to the non-cancer samples in the training set. A tissue of origin or cancer type assessment was subsequently made for each training sample determined to be positive for cancer from the multiclass classifier. First, the multiclass logistic regression classifier assigned a set of probability scores, one for each prospective cancer type, to each sample. Next, the confidence of these scores was assessed as the difference between the highest and second-highest scores. A threshold was prespecified as the minimum value such that, of the cancer samples in the training set with a top-two score differential exceeding the threshold, 90% had been had beenassigned assignedthethe correct TOO label correct as their TOO label as highest score. For their highest prediction score. on For prediction on new samples, samples with a top-two score differential lower than this threshold received an assignment of "indeterminate cancer"; those whose score differential exceeded the threshold were assigned the cancer label to which the multiclass classifier assigned the highest score.

6.6.8. Example 8: Classifier performance

[00322] As a proxy for testing different probe sets (bait sets), classifier

performance was determined for each of Assay Panels 1-6 by constraining the data

set and only considering the methylation status of CpG sites within fragments that

would be enriched for by the specified Assay Panels.

[00323] The results of the classifier performance analysis are provided in

Tables 11 to 18. For each set of genomic regions (i.e., Assay Panels 1-6), the

Detection column of the tables indicates the performance of the classifier over the

indicated Assay Panels. Here, Detection is the sensitivity for all cancer types

included in the CCGA2 study stratified by cancer stage at 99% specificity. The

Tissue of Origin column indicates the accuracy of a tissue of origin classification at

99% specificity only for individuals diagnosed with cancer and when sufficient

methylation data was available to assign a tissue of origin. Values indicate mean

accuracy, with a 95% confidence interval in brackets and the number correctly

assigned divided by the total in the category in parenthesis.

Table 11. Classification accuracy using the genomic regions of Assay Panel 1 wo 2020/069350 WO PCT/US2019/053509

Stage Detection* Tissue of Origin* I (68/422) [63.2-87.5] (40/52) 16.1% (12.7-20) 12.7-20] (68/422) 76.9% 63.2-87.5 (40/52) II (35.5-45.5) (157/388) (157/388) (83.1-94.2)

[83.1-94.2] (120/134) 40.5% 35.5-45.5 89.6% III 70.6-80.4 (237/313) (85.5-93.9) (194/215) (194/215) 75.7% 70.6-80.41 75.7% 90.2% 85.5-93.9 IV (85.6-92.2) 89.3% 85.6-92.2 (324/363) (88.3-94.8) 91.9% 88.3-94.8 (274/298) All [49.6-54.7] 52.2% 49.6-54.7 (799/1532) (87.4-92] (640/712) 89.9% 87.4-92 (640/712)

Table 12. Classification accuracy using the genomic regions of Assay Panel 2

Stage Detection* Tissue of Origin* I I 19.2% [15.5-23.3] 15.5-23.31 (81/422) 68.9% [55.7-80.1] (55.7-80.1) (42/61) (42/61) II 38.3-48.41 (168/388) 38.3-48.4 [79.8-91.5] (79.8-91.5) (127/147) 43.3% 86.4% III 72.9-82.41 (244/313) 72.9-82.4] 87-94.8] (203/222) 78% 91.4% 87-94.8 IV 90.1% (86.5-93) (327/363) 90.1% 86.5-93 [91-96.6] 94.3% 91-96.61 (280/297) All 54.6% 52-57.17 54.6% (836/1532) 52-57.1 (836/1532) (87.3-91.8) (666/742) 89.8% 87.3-91.8 (666/742)

Table 13. Classification accuracy using the genomic regions of Assay Panel 3

Stage Detection Detection* Tissue of Origin* I 19,2% 19.2% (15.5-23.3) 15.5-23.3 (81/422) 76.3% [63.4-86.4] 63.4-86.41 (45/59) (45/59) II 43.6% [38.6-48.7] 43.6% 38.6-48.7] (169/388) (79.8-91.3) 86.3% 79.8-91.3 86,3% (132/153) III (86.6-94.4) (213/234) 79.9% 175-84.21 79.9% 75-84.2 (250/313) 91% [86.6-94.4] 91% (213/234)

IV 90.6% (87.2-93.4) 90.6% 87.2-93.4 (329/363) 92.6% (89.1-95.3)

[89.1-95.3] (289/312) (289/312) All All 55.4% (52.9-57.9) 55.4% 52.9-57.9 (849/1532) [87.3-91.8] 87.3-91.8 (697/777) 89.7%

Table 14. Classification accuracy using the genomic regions of Assay Panel 4

Stage Detection* Tissue of Origin* I 22% (18.2-26.3)

[18.2-26.3](93/422) (93/422) (59.3-82](48/67) 59.3-82 (48/67) 22% 71.6% II 44.3% [39.3-49.4]

[39.3-49.4] (172/388) (172/388) 86.2% [79.7-91.2] (131/152)

[79.7-91.2] (131/152) III [87.3-94.9] 81.2% [76.4-85.3] 81.2% 76.4-85.3 (254/313) 91.7% 87.3-94.91 (209/228)

IV (86.8-93.2) (328/363) 90.4% (86.8-93.2) 93.4% [90-95.9] 90-95.91 (284/304) All 56.8% [54.3-59.3] 56.8% 54.3-59.3 (870/1532) 89.6% [87.3-91.7] (691/771)

Table 15. Classification accuracy using the genomic regions of Assay Panel 5

Stage Detection* Tissue of Origin* I 18-26] (92/422) (52/69) 21.8% 18-261 (92/422) 75.4% 63.5-84.9 63.5-84.91(52/69) II [38.3-48.4] 38.3-48.4 (168/388) [83.1-93.5] (83.1-93.5) (139/156) (139/156) 43.3% 89.1% III [77.8-86.5] (258/313) (214/232) 82.4% 82.4% 77.8-86.5 92.2% 788-95.31 88-95.3 (214/232) IV 90.4% (86.8-93.2) 86.8-93.2 (328/363) 788-94.57 (286/312) 91.7% 88-94.5 (286/312) All All 56.7% 56.7% (54.2-59.2) 54.2-59.2] (869/1532) (87.3-91.7) (709/791) 89.6% [87.3-91.7] (709/791)

Table 16. Classification accuracy using the genomic regions of Assay Panel 6

Stage Detection* Tissue of Origin* I (18.2-26.3) (93/422) 18.2-26.3 (93/422) [63.9-84.7] 63.9-84.71 (55/73) (55/73) 22% 22% 75.3% II (40.6-50.7) (177/388)

[40.6-50.7] (177/388) (145/166) 81.3-92] (145/166) 45,6% 45.6% 87.3% 81.3-92 III 81.8% (77.1-85.9) 77.1-85.9 (256/313) 91.7% (87.5-94.9) (222/242) 87.5-94.9 IV 90.1% 86.5-931 (327/363) 86.5-93 (327/363) 92.1% 88.6-94.8 (292/317) 88.6-94.8 All 57.1% [54.6-59.6] 54.6-59.6 (875/1532) 89.5% (87.2-91.5) (733/819) 87.2-91.5 (733/819)

Table 17. Classification accuracy using the genomic regions of Assay Panel 3A

Stage Detection* Tissue of Origin* I [12.7-20] (68/422) [57.7-83.2] (38/53) 16.1% [12.7-20] (68/422) 71.7% II [35.3-45.3] 80.4-92](127/146) (127/146) 40.2% 35.3-45.3 (156/388) 87% 80.4-92 87% III (87.5-95.3) (195/212) (195/212) 74.8% [69.6-79.5] 69.6-79.5 (234/313) 92% 87.5-95.3 92% (84.4-91.31 (320/363) [88.1-94.7]

[88.1-94.7] (269/293) IV 88.2% 84.4-91.3 88.2% 91.8% All 52.2% 49.6-54.71 52,2% 49.6-54.7 (799/1532) 89.3% (86.9-91.5)

[86.9-91.5] (645/722) (645/722)

Table 18. Classification accuracy using the genomic regions of Assay Panel 4A

Stage Detection* Tissue of Origin* I (13.8-21.3) 13.8-21.3 (73/422) (60.3-85) (40/54) (40/54) 17.3% 74.1% 74.1% 60.3-85 II (158/388) 82.2-93.21 (131/148) (131/148) 40.7% 35.8-45.87 35.8-45.8 88,5% 88.5% 82.2-93.2 III 79.2% (74.3-83.6) 74.3-83.6 (248/313) 89.7% [85-93.3] 85-93.3 (208/232)

IV (85.3-92) (323/363)

[85.3-92] (323/363) 88.7-951 (286/310) 88.7-95 89% 89% 92.3% All 53.7% (51.1-56.2) (822/1532) 53.7% 89.4% 87-91.5] 87-91.5 (682/763)

[00324] These results show that the accuracy of detection and tissue of origin

determination is higher for later stage cancers than early stage cancers.

Additionally, larger panels apparently provide more accurate cancer detection for

stage I and stage II cancers.

[00325] Part A of FIGS. 14-21 presents receiver operator curves (ROC curves)

for Assay Panels 1-6 and 3A and 4A from the binary logistic regression classifier

using mixture model based featurization described above. The ROC curve results

were similar for all Assay Panels, showing an area under the curves (AUC) falling

within a narrow range of 0.80 to 0.83. Part B of FIGS. 14-21 presents confusion

matrices showing tissue of origin (TOO) accuracy determined for the multiclass

logistic regression classifier trained using mixture model-based features, as

described above. As shown in FIGS. 14-21, TOO precision ranged from 89.9% to

91.1% across Assay Panels 1-6

105 wo 2020/069350 WO PCT/US2019/053509

[00326] The classification performance for random subsets of the markers in

Assay Panels 3-5, at 99% specificity, are presented in Tables 19-24.

Table 19. Classification accuracy using a randomly selected subset of 40% of the

genomic regions of Assay Panel 3 (subset a)

Stage Detection* Tissue of Origin* I I (14.9-22.5) (78/422) 18.5% [14.9-22.5] (78/422) [59.7-84.2] (41/56) (41/56) 18.5% 73.2% 59.7-84.2 II [37-47.1](163/388) 42% [37-47.1] 42% (163/388) 87.9% [81.3-92.8] 81.3-92.8 (123/140) III [88.2-95.7] (88.2-95.7) (200/216) 79.6% (74.7-83.9) (249/313) 74.7-83.9 (249/313) 92.6% IV 89.5% 85.9-92.51 (325/363) 89.5% 85.9-92.5 (325/363) 91% [87.2-94] 91% (274/301) 87.2-94](274/301) All (87.1-91.7) (654/730) (654/730) 54.4% 51.9-57] (834/1532) 54.4% 89.6% 89.6%[87.1-91.7]

Table 20. Classification accuracy using a randomly selected subset of 40% of the

genomic regions of Assay Panel 3 (subset b)

Stage Detection* Tissue of Origin* I (14.2-21.8) 14.2-21.8 (75/422) [61.6-85.6] (42/56) 61.6-85.6 (42/56) 17.8% 75% II 41.5% [36.5-46.61 41.5% 36.5-46.6 (161/388) 85.9% [79.1-91.2]

[79.1-91.2] (122/142) (122/142) III [72.6-82.1] (243/313) [88-95.5] 77.6% 77.6% 72.6-82.1 92.3% 88-95.5 (205/222) IV 89.3% 89.3% (85.6-92.2) 85.6-92.2 (324/363) 91.7% [88.1-94.6] 88.1-94.6 (278/303) (278/303) All (51.1-56.1) All 53.6% 53.6% 51.1-56.1 (821/1532) 89.5% [87-91.6] 87-91.61 (663/741)

Table 21. Classification accuracy using a randomly selected subset of 40% of the

genomic regions of Assay Panel 3 (subset c)

Stage Detection* Tissue of Origin* I [13.4-20.7] 13.4-20.7] (71/422) [66.5-89.4] (43/54) (43/54) 16.8% 79.6% II (165/388) (78.2-90.41 (125/147) 42.5% [37.6-47,6] 37.6-47.6 85% [78.2-90.4] 85% (125/147) III (72.3-81.8) (242/313) (87.2-95.2) (191/208)

[87.2-95.2] (191/208) 77.3% 72.3-81.8 77.3% 91.8% IV 89.5% (85.9-92.5) 85.9-92.5 (325/363) 92.3% [88.6-95] (275/298) All All (51.2-56.2] (823/1532) 53.7% 51.2-56.2 (823/1532) 89.7% (87.2-91.8] (650/725)

[87.2-91.8] (650/725)

Table 22. Classification accuracy using a randomly selected subset of 60% of the

genomic regions of Assay Panel 3

Stage Detection* Tissue of Origin* I 18% 14.5-22] (76/422) (76/422) [57.8-82.7] (40/56)

[57.8-82.77 (40/56) 18%[14.5-22] 71.4% II 41.2% [36.3-46.3]

[36.3-46.3] (160/388) (160/388) 86% [79.2-91.2] 86% 79.2-91.21(123/143) (123/143) III 78.6% [73.6-83] 78.6% (246/313) 73.6-83 (246/313) 91.1% (86.6-94.5) (205/225) 86.6-94.5 (205/225)

IV (85.9-92.5) 89.5% 85.9-92.5 (325/363) 89.5% 91.6% 87.8-94.5 (272/297) (87.8-94.5)

All 54% 51.5-56.61 (828/1532)

[51.5-56.6] (828/1532) 88.7% [86.2-90.9] (86.2-90.9) (658/742)

WO wo 2020/069350 PCT/US2019/053509

Table 23. Classification accuracy using a randomly selected subset of 50% of the

genomic regions of Assay Panel 4

Stage Detection* Tissue of Origin* I 18.5% (14.9-22.5) (78/422) 14.9-22.5 (78/422) 80.7% 68.1-90] (46/57) 68.1-90 II II 44.3% (39.3-49.4) 39.3-49.4 (172/388) 82.9% [76-88.5] 76-88.5 (126/152) III 87.3-95] (200/218) 78.9% 74-83.3 (247/313) 78.9% 74-83.31 91.7% 87.3-95 IV (86.8-93.2) 90.4% 86.8-93.2 (328/363) 92.4% [88.9-95.1] (88.9-95.1) (281/304) All 55.3% [52.8-57.8] 52.8-57.8 (847/1532) 89.5% [87-91.6] 87-91.6 (670/749)

Table 24. Classification accuracy using a randomly selected subset of 50% of the

genomic regions of Assay Panel 5

Stage Detection* Tissue of Origin* I 20.4% 16.6-24.5] 16.6-24.5 (86/422) 73.8% 61.5-84 73.8% (48/65) 61.5-84 (48/65) II (38.3-48.4) (168/388) (80.7-91.91 (134/154) (134/154) 43,3% 43.3% 38.3-48.4 87% 80.7-91.9 87% III (75.3-84.5) (86.2-94.2) (206/227) 80.2% [75.3-84.5] 80.2% (251/313) 90.7% [86.2-94.2] (206/227)

IV 85.3-92] (323/363) 85.3-92] (323/363) 92.7% (89.2-95.4) (281/303) 89.2-95.4 (281/303) 89% 89% All (52.8-57.9)(848/1532) 55.4% [52.8-57.9] 55.4% (848/1532) 89.4% 787-91.51 (686/767)

[87-91.5] (686/767)

[00327] Part A of FIGS. 22-27 presents receiver operator curves (ROC curves)

for randomlyselected for randomly selected subsets subsets of Assay of Assay PanelsPanels 3-4thefrom 3-4 from the binary trained trained binary logistic logistic

regression classifier using mixture model based featurization, as described above.

Classifier performance results for the same random subsets the markers are also

shown in Tables 19-24. As shown, the areas under the curve (AUC) for these

receiver operator curves (ROC curves) were similar to each other and similar to the

areas under the curve (AUC) for the complete assay panels. A summary of the area

under the curve results (AUC) for each Assay Panel is presented in Table 25. Part

B of FIGS. 22-27 presents confusion matrices showing tissue of origin (TOO)

accuracy determined for the multiclass logistic regression classifier described

above. As shown in FIGS. 22-27, TOO precision ranged from 90.4% to 90.7% for

the randomly selected subsets of Assay Panels 3-4.

Table 25. Areas under the curves for cancer classification.

Assay Panel 1 AUC 0.81

2 0.82 3 0.82 4 0.83 5 0.83 6 0.83

3A 0.80 3A 0.82 4A 3 Random 40% a 0.81 0.81 3 Random 40% b 0.82 3 Random 40% C c 0.82 3 Random 60% 0.82

4 Random 50% 0.82 5 Random 50% 0.78

[00328] Table 26 shows tissue of origin accuracy for each cancer type at a

specificity of 0.994 for Assay Panels 1, 3A and 5. Each column shows TOO

precision with a 95% confidence interval in brackets and in parenthesis the number

correctly assigned divided by the total number of samples correctly detected as

having cancer under the binary logistic regression classifier described above.

Table 26. TOO precision for Assay Panels 1, 3A, and 5

Cancer Type Assay Panel 1 Assay Panel 3A Assay Panel 5 97.3% [93.7-

Lung 97% [93.2-99] 97% [93.2-99] 99.1]

(163/168) (163/168) (178/183) 97.7% [91.9- Colorectal 97.8% 97.8% [92.2-99.7]

[92.2-99.7] 99.7] 98.9% [94.3-100] (88/90) (84/86) (94/95)

Lymphoid neoplasm 100% [95.4-100] 100% [95.9-100] 100% [96-100] (79/79) (89/89) (90/90)

Breast 98.6% [92.3-100] 98.6% [92.2-100] 98.8% [93.5-100] (69/70) (68/69) (82/83)

90.9% [81.3- 90.5% [81.5-

Pancreas 87.7% [77.2-94.5] 96.6] 96.1] Gallbladder (57/65) (60/66) (67/74)

85.1% [71.7- 88.2% [76.1-

Head and Neck 89.8% [77.8-96.6] 93.8] 95.6] (44/49) (40/47) (45/51)

89.8% [77.8- 86.5% [74.2-

Upper GI 80.9% [66.7-90.9] 96.6] 94.4] (38/47) (44/49) (45/52)

88.9% [70.8-

Ovary 86.4% [65.1-97.1] 91.3% [72-98.9] 97.6] (19/22) (21/23) (24/27) Liver / Bile Duct 77.8% [52.4-93.6] 70.8% [48.9- 77.3% [54.6- (14/18) 87.4] 92.2]

WO wo 2020/069350 PCT/US2019/053509

(17/24) (17/22)

92.9% [66.1- 95.2% [76.2- Uterine 100% [75.3-100] 99.8] 99.9] (13/13) (13/14) (20/21)

87.5% [61.7- 94.4% [72.7- Prostate 92.3% [64-99.8] 98.4] 99.9] (12/13) (14/16) (17/18)

Multiple Myeloma 92.3% [64-99.8] 94.7% [74-99.9] 91.3% [72-98.9] (12/13) (18/19) (21/23)

Renal 100% [69.2-100] 100% [66.4-100] 100% [73.5-100] (10/10) (9/9) (12/12)

Anorectal 0% [0-33.6] 12.5% [0.3-52.7] 0% [0-45.9] (0/6) (0/9) (1/8)

Bladder and 0% [0-60.2] 40% [5.3-85.3] 50% [11.8-88.2] urothelia (0/4) (2/5) (3/6)

Sarcoma 66.7% [9.4-99.2] 25% [0.6-80.6] 50% [6.8-93.2] (2/3) (1/4) (2/4)

Cervical 0% [0-97.5] 100% [15.8-100] 25% [0.6-80.6] (0/1) (2/2) (1/4)

Melanoma 100% [2.5-100] 66.7% [9.4-99.2] 100% [29.2-100] (1/1) (2/3) (3/3)

[00329] Tables 27-29 show classifier performance for the binary logistic

regression classifier described above for Assay Panels 1, 3A and 5. Each tables

shows the sensitivity for each cancer type, stratified by stage, at a specificity of

0.994. Each column also shows the 95% confidence interval in brackets and in

parenthesis the number correctly detected divided by the total number of samples

in the data set for each cancer type, at stages I through IV.

Table 27. Classifier performance by cancer type and stage for Assay Panel 1

I Il III III Cancer Type IV All Stages

Lung 16.7% 16.7% 78.3% 83.3% 86.8% 69%

[8.3-28.5] [56.3-92.5] [72.7-91.1] [78.8-92.6] [63-74.5] (10/60) (18/23) (60/72) (92/106) (180/261) Prostate 0% 0.9% 10.5% 76.5% 8.5% 0%

[0-9] [0-4.8] [1.3-33.1]

[1.3-33.1] [50.1-93.2] [4.9-13.5] (0/39) (1/113) (2/19) (13/17) (16/188)

Breast HR 2.3% 20% 71.4% 100% 18.1% positive [0.3-8.1] [11.4-31.3]

[11.4-31.3] [41.9-91.6] [54.1-100] [12.7-24.6]

[12.7-24.6] (2/87) (14/70) (10/14) (6/6) (32/177)

Lymphoid 20% 74.1% 70.4% 76.9% 56.5% neoplasm [4.3-48.1] [53.7-88.9] [49.8-86.2] [60.7-88.9] [48-64.6]

[48-64.6] (3/15) (20/27) (19/27) (30/39) (83/147)

Colorectal 41.2% 66.7% 80.4% 93.3% 77%

[18.4-

[18.4- [46-83.5] [66.1-90.6] [81.7-98.6] [69-83.8] 67.1] (18/27) (37/46) (42/45) (104/135) (7/17)

58.3% Pancreas [27.7- 71.4% 81.2% 92.9% 82.1% 84.8] [41.9-91.6] [54.4-96] [80.5-98.5] [72.3-89.6] (7/12) (10/14) (13/16) (39/42) (69/84)

Endometrial 13.7% 13.7% 33.3% 60% 66.7% 19% carcinoma [6.8-23.8] [0.8-90.6] [14.7-94.7] [9.4-99.2] [11.3-29.1] (10/73) (1/3) (3/5) (2/3) (16/84)

Breast HR 0% 62.5% 100% 100% 100% 57.1% negative [0-21.8]

[0-21.8] [45.8-77.3] [75.3-100] [15.8-100] [44.7-68.9] (0/15) (25/40) (13/13) (2/2) (2/2) (40/70)

85.7% Head and [42.1-

[42.1- 76.9% 87.5% 88.5% 85.5% Neck 99.6] [46.2-95] [61.7-98.4] [69.8-97.6] [74.2-93.1] (6/7) (10/13) (14/16) (23/26) (53/62)

Renal 0% 0% 50% 81.8% 19.6% 19.6%

[0-9.5] [0-60.2] [6.8-93.2] [48.2-97.7] [10.2-32.4] (0/37) (0/4) (2/4) (9/11) (11/56)

Ovary 25% 0% 68% 100% 64.9%

[0.6-80.6] [0-84.2] [46.5-85.1] [54.1-100] [47.5-79.8] (1/4) (0/2) (17/25) (6/6) (24/37)

Multiple 20% 23.1% 23.1% 90.9% 44.1% Myeloma [2.5-55.6] [5-53.8] [58.7-99.8] [27.2-62.1] (2/10) (3/13) (10/11) - (15/34)

16.7% 16.7% 82.4% 100%

[0.4-64.1] 75% [34.9- [56.6-96.2] [82.4-100] 80% [66.3-

Esophagus (1/6) 96.8] (6/8) (14/17) (19/19) 90] (40/50)

20% [2.5- 23.1% [5- 90.9% 44.1% Multiple 55.6] 53.8] [58.7-99.8] [27.2-62.1]

myeloma (2/10) (3/13) (10/11) - (15/34)

100% 75% [53.3- 100% 25% [0.6- 80% [28.4- 60% [14.7- [69.2-100] 90.2] Biliary tract 80.6] (1/4) 99.5] (4/5) 94.7] (3/5) (10/10) (18/24)

100% 100% 76.5% 0% [0- 75% [19.4- [15.8-100] [63.1-100] [50.1-93.2] Gastric 70.8] (0/3) 99.4] (3/4) (2/2) (8/8) (13/17)

100% 100% 75% [47.6- 100% 25% [0.6- 66.7% [9.4- [47.8-100] [39.8-100] 92.7] Hepatocellular 80.6] (1/4) 99.2] (2/3) (5/5) (5/5) (4/4) (12/16)

66.7% 37.5% 100% [2.5- 0% [0- 20% [0.5- [22.3-95.7] [15.2-64.6]

Sarcoma 100] (1/1) 60.2] (0/4) 71.6] (1/5) (4/6) (6/16)

60% 66.7% 50% 0% 54.5%

[14.7-

[14.7- [9.4-99.2] [1.3-98.7] [0-97.5] [23.4-83.3]

[23.4-83.3]

Bladder 94.7] (3/5) (2/3) (1/2) (0/1) (6/11)

12.5% 100% 100% 36.4%

[0.3-52.7] [2.5-100] [15.8-100] [10.9-69.2]

[10.9-69.2] Cervical (1/8) (1/1) (2/2) - (4/11)

0% 0% 100% 42.9%

[0-70.8] [0-97.5] [29.2-100] [9.9-81.6]

Melanoma (0/3) (0/1) - - (3/3) (3/7)

100% 50% 60% Renal Pelvis [2.5-100] [6.8-93.2] [14.7-94.7] Urothelial (1/1) (2/4) - - (3/5)

100% 100% 100% 100% Uterine [2.5-100] [2.5-100] (1/1) (1/1) sarcoma - - -

0% Myeloid [0-60.2]

neoplasm - - - -- (0/4)

0% 0% 0% 0%

[0-84.2] [0-97.5] [0-97.5] [0-60.2]

Thyroid (0/2) (0/1) - (0/1) (0/4)

Table 28. Classifier performance by cancer type and stage for Assay Panel 3A

I Il III Cancer Type IV All Stages

Lung 13.3% 13.3% 73.9% 79.2% 86.8% 66.7% 66.7%

[5.9-24.6] [51.6-89.8] [68-87.8] [78.8-92.6] [60.6-72.4] (8/60) (17/23) (57/72) (92/106) (92/106) (174/261) Prostate 1.8% 1.8% 10.5% 10.5% 76.5% 0% 9%

[0-9]

[0-9] [0.2-6.2] [1.3-33.1] [50.1-93.2] [5.4-14.1] (0/39) (2/113) (2/19) (13/17) (17/188)

Breast HR 2.3% 18.6% 71.4% 83.3% 16.9% 16.9% positive [0.3-8.1] [10.3-29.7] [41.9-91.6] [35.9-99.6] [11.7-23.3] (2/87) (13/70) (10/14) (5/6) (30/177)

Lymphoid 26.7% 81.5% 70.4% 74.4% 61.9% neoplasm [7.8-55.1] [61.9-93.7] [49.8-86.2] [57.9-87] [53.5-69.8] (4/15) (22/27) (19/27) (29/39) (91/147)

35.3% Colorectal [14.2- 51.9% 51.9% 78.3% 93.3% 72.6% 61.7] [31.9-71.3] [63.6-89.1] [81.7-98.6] [64.3-79.9] (6/17) (14/27) (36/46) (42/45) (98/135)

Pancreas 33.3% 71.4% 75% 92.9% 77.4%

[9.9-65.1] [41.9-91.6] [47.6-92.7] [80.5-98.5] [67-85.8] (4/12) (10/14) (12/16) (39/42) (65/84)

Endometrial 15.1% 15.1% 33.3% 60% 33.3% 19% carcinoma [7.8-25.4] [0.8-90.6] [14.7-94.7] [0.8-90.6] [11.3-29.1] (11/73) (1/3) (3/5) (1/3) (16/84)

Breast HR 0% 65% 100% 100% 58.6% negative [0-21.8]

[0-21.8] [48.3-79.4] [75.3-100] [15.8-100] [46.2-70.2] (0/15) (26/40) (13/13) (2/2) (41/70)

Head and 85.7% 84.6% 81.2% 88.5% 85.5%

[42.1-

[42.1- [54.6-98.1] [54.4-96] [69.8-97.6] [74.2-93.1] Neck 99.6] (6/7) (11/13) (13/16) (23/26) (53/62)

Renal 0% 0% 25% 50% 72.7% 19.6% 19.6%

[0-9.5] [0.6-80.6] [6.8-93.2] [39-94] [10.2-32.4] (0/37) (1/4) (2/4) (8/11) (11/56)

Ovary 25% 0% 72% 100% 67.6%

[0.6-80.6] [0-84.2] [50.6-87.9] [54.1-100] [50.2-82] (1/4) (0/2) (18/25) (6/6) (25/37)

40% Multiple [12.2- 46.2% 100% 61.8% 100% Myeloma 73.8] [19.2-74.9] [71.5-100] [43.6-77.8] (4/10) (6/13) (11/11) - - (21/34)

Esophagus 16.7% 16.7% 75% 82.4% 94.7% 78%

[0.4-64.1] [34.9-96.8] [56.6-96.2] [74-99.9] [64-88.5] (1/6) (6/8) (14/17) (18/19) (39/50)

40%

[12.2- 46.2% 100% 61.8% Multiple 73.8] [19.2-74.9] [71.5-100] [43.6-77.8]

myeloma (4/10) (6/13) (11/11) - (21/34)

25% 80% 60% 100% 75%

[0.6-80.6] [28.4-99.5] [14.7-94.7] [69.2-100] [53.3-90.2] Biliary tract (1/4) (4/5) (3/5) (10/10) (18/24)

Gastric 100% 100% 100% 100% 82.4% 0%

[0-70.8] [39.8-100] [15.8-100] [63.1-100] [56.6-96.2] (0/3) (4/4) (2/2) (8/8) (14/17)

Hepatocellular 50% 100% 100% 100% 87.5%

[6.8-93.2] [29.2-100] [47.8-100] [39.8-100] [61.7-98.4] (2/4) (3/3) (5/5) (4/4) (14/16)

Sarcoma 0% 0% 0% 20% 50% 25%

[0-97.5] [0-60.2] [0.5-71.6] [11.8-88.2] [7.3-52.4] (0/1) (0/4) (1/5) (3/6) (4/16)

Bladder 40% 66.7% 50% 0% 45.5%

[5.3-85.3] [9.4-99.2] [1.3-98.7] [0-97.5] [16.7-76.6] (2/5) (2/3) (1/2) (0/1) (5/11)

Cervical 12.5% 100% 100% 36.4% 100%

[0.3-52.7] [2.5-100] [15.8-100] [10.9-69.2] (1/8) (1/1) (2/2) - (4/11)

Melanoma 0% 0% 100% 42.9%

[0-70.8] [0-97.5] [29.2-100] [9.9-81.6] (0/3) (0/1) - (3/3) (3/7)

100% 25% 40% Renal Pelvis [2.5-100] [0.6-80.6] [5.3-85.3] Urothelial (1/1) (1/4) - - - (2/5)

100% 100% 100% Uterine [2.5-100] [2.5-100]

sarcoma (1/1) - (1/1) sarcoma - -

0% Myeloid [0-60.2]

neoplasm - - - - - I (0/4)

0% 0% 0% 0%

[0-84.2]

[0-84.2] [0-97.5] [0-97.5] [0-60.2]

Thyroid (0/2) (0/1) - (0/1) (0/4)

Table 29. Classifier performance by cancer type and stage for Assay Panel 5

I II Il III III Cancer Type IV All Stages

20% Lung [10.8- 78.3% 86.1% 91.5% 72.4% 32.3] [56.3-92.5] [75.9-93.1] [84.5-96] [66.6-77.7] (12/60) (18/23) (62/72) (97/106) (189/261) Prostate 2.6% 4.4% 10.5% 10.5% 76.5% 11.2% 11.2%

[0.1-13.5] [1.5-10] [1.3-33.1] [50.1-93.2] [7-16.6] (1/39) (5/113) (2/19) (13/17) (21/188)

Breast HR 4.6% 25.7% 71.4% 100% 21.5% positive [1.3-11.4] [16-37.6] [41.9-91.6] [54.1-100] [15.7-28.3] (4/87) (18/70) (10/14) (6/6) (38/177)

Lymphoid 26.7% 74.1% 77.8% 79.5% 62.6% neoplasm [7.8-55.1] [53.7-88.9] [57.7-91.4] [63.5-90.7] [54.2-70.4] (4/15) (20/27) (21/27) (31/39) (92/147)

Colorectal 47.1% 70.4% 82.6% 93.3% 79.3%

[23-72.2] [49.8-86.2] [68.6-92.2] [81.7-98.6] [71.4-85.8] (8/17) (19/27) (38/46) (42/45) (107/135) 58.3% 58.3% Pancreas [27.7-

[27.7- 84.5% 71.4% 87.5% 95.2% 84.5% 84.8] [41.9-91.6] [61.7-98.4] [83.8-99.4] [75-91.5] (7/12) (10/14) (14/16) (40/42) (71/84)

Endometrial 20.5% 33.3% 60% 66.7% 25% [16.2-

carcinoma [12-31.6] [0.8-90.6] [14.7-94.7] [9.4-99.2] 35.6] (15/73) (1/3) (3/5) (2/3) (21/84)

Breast HR 6.7% 72.5% 100% 100% 100% 64.3% negative [0.2-31.9] [56.1-85.4] [75.3-100] [15.8-100] [51.9-75.4] (1/15) (29/40) (13/13) (2/2) (45/70)

Head and 100% 100% 84.6% 87.5% 88.5% 88.7% Neck [59-100] [54.6-98.1]

[54.6-98.1] [61.7-98.4] [69.8-97.6] [78.1-95.3] (7/7) (11/13) (14/16) (23/26) (55/62)

Renal Renal 50% 50% 81.8% 23.2% 0%

[0-9.5] [6.8-93.2] [6.8-93.2]

[6.8-93.2] [48.2-97.7] [13-36.4] (0/37) (2/4) (2/4) (9/11) (13/56)

Ovary 25% 0% 88% 100% 78.4%

[0.6-80.6] [0-84.2] [68.8-97.5] [54.1-100] [61.8-90.2] (1/4) (0/2) (22/25) (6/6) (29/37)

40% Multiple [12.2- 46.2% 100% 61.8% 61.8% Myeloma 73.8] [19.2-74.9] [71.5-100] [43.6-77.8] (4/10) (6/13) (11/11) - (21/34)

Esophagus 25% 80% 100% 100% 75% [53.3- 60%

[0.6-80.6] [28.4-99.5] [14.7-94.7] [69.2-100] 90.2] (1/4) (4/5) (3/5) (10/10) (18/24)

0% 100% 100% 100% 82.4% Multiple [0-70.8] [39.8-100] [15.8-100] [63.1-100] [56.6-96.2]

myeloma (0/3) (4/4) (2/2) (8/8) (14/17)

50% 100% 100% 100% 100% 100% 87.5% 87.5%

[6.8-93.2] [29.2-100] [47.8-100] [39.8-100] [61.7-98.4] Biliary tract (2/4) (3/3) (5/5) (4/4) (14/16)

Gastric

0% 0% 0% 20% 50% 25%

WO wo 2020/069350 PCT/US2019/053509

[0-97.5] [0-60.2] [0.5-71.6]

[0.5-71.6] [11.8-88.2] [7.3-52.4] (0/1) (0/4) (1/5) (3/6) (4/16)

Hepatocellular 40% 66.7% 50% 45.5% 0%

[5.3-85.3] [9.4-99.2] [1.3-98.7] [0-97.5] [16.7-76.6] (2/5) (2/3) (1/2) (0/1) (0/1) (5/11)

Sarcoma 12.5% 12.5% 100% 100% 36.4%

[0.3-52.7] 100% [2.5- 100%[2.5- [15.8-100] [10.9-69.2] (1/8) 100] (1/1) (2/2) - (4/11)

Bladder 0% 0% 100% 42.9%

[0-70.8] [0-97.5] [29.2-100] [9.9-81.6] (0/3) (0/1) - (3/3) (3/7)

Cervical 100% 25% 40% 100%

[2.5-100] [0.6-80.6]

[0.6-80.6] [5.3-85.3] (1/1) (1/4) - -- (2/5)

Melanoma 100% 100% 100%

[2.5-100] [2.5-100] (1/1) -- - -- (1/1)

0% Renal Pelvis [0-60.2] Urothelial - - - - - (0/4)

0% 0% 0% 0% Uterine [0-84.2] [0-97.5] [0-97.5] [0-60.2] (0/2) (0/1) (0/1) (0/4) sarcoma sarcoma -

40%

[12.2- 46.2% 100% 61.8% Myeloid 73.8] [19.2-74.9] [71.5-100] [43.6-77.8]

neoplasm neoplasm (4/10) (6/13) (11/11) -- (21/34)

25% 100% 75% [53.3-

[0.6-80.6] 80% [28.4- 60% [14.7- [69.2-100] 90.2]

Thyroid (1/4) 99.5] (4/5) 94.7] (3/5) (10/10) (18/24)

7. INCORPORATION BY REFERENCE

[00330] All publications, patents, patent applications and other documents

cited in this application are hereby incorporated by reference in their entireties for

all purposes to the same extent as if each individual publication, patent, patent

application or other document were individually indicated to be incorporated by

reference for all purposes.

8. 8. EQUIVALENTS

[00331] It is to be understood that the figures and descriptions of the present

disclosure have been simplified to illustrate elements that are relevant for a clear

understanding of the present disclosure, while eliminating, for the purpose of

clarity, many other elements found in a typical system. Those of ordinary skill in the art may recognize that other elements and/or steps are desirable and/or required in implementing the present disclosure. However, because such elements and steps are well known in the art, and because they do not facilitate a better understanding of the present disclosure, a discussion of such elements and steps is not provided herein. The disclosure herein is directed to all such variations and modifications to such elements and methods known to those skilled in the art.

[00332] Some portions of above description describe the embodiments in terms

of algorithms and symbolic representations of operations on information. These

algorithmic descriptions and representations are commonly used by those skilled in

the data processing arts to convey the substance of their work effectively to others

skilled in the art. These operations, while described functionally, computationally,

or logically, are understood to be implemented by computer programs or

equivalent electrical circuits, microcode, or the like. The described operations and

their associated modules may be embodied in software, firmware, hardware, or any

combinations thereof.

[00333] As used herein any reference to "one embodiment" or "an

embodiment" means that a particular element, feature, structure, or characteristic

described in connection with the embodiment is included in at least one

embodiment. The appearances of the phrase "in one embodiment" in various places

in the specification are not necessarily all referring to the same embodiment,

thereby providing a framework for various possibilities of described embodiments

to function together.

[00334] As used herein, the terms "comprises," "comprising," "includes,"

"including," "has," "having" or any other variation thereof, are intended to cover a

non-exclusive inclusion. For example, a process, method, article, or apparatus that

comprises a list of elements is not necessarily limited to only those elements but

may include other elements not expressly listed or inherent to such process,

method, article, or apparatus. Further, unless expressly stated to the contrary, "or"

refers to an inclusive or and not to an exclusive or. For example, a condition A or B

is satisfied by any one of the following: A is true (or present) and B is false (or not

present), A is false (or not present) and B is true (or present), and both A and B are

true (or present).

115

[00335] In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the

description. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.

[00336] While particular embodiments and applications have been illustrated and described, it is 2019351130

to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to

those skilled in the art, may be made in the arrangement, operation and details of the method and

apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.

[00337] While various specific embodiments have been illustrated and described, the above

specification is not restrictive. It will be appreciated that various changes can be made without departing from the spirit and scope of the description(s). Many variations will become apparent to those skilled in

the art upon review of this specification.

[00338] The reference to any prior art in this specification is not, and should not be taken as, an

acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge in

Australia.

Claims

CLAIMS 02 Oct 2025 WHAT IS CLAIMED IS:

1. A mixture comprising (a) an assay panel for enriching target polynucleotides for detecting cancer and/or a type of cancer in a subject, and (b) cell-free DNA (cfDNA) molecules or amplification products thereof; 2019351130

wherein the assay panel comprises at least 1,000 polynucleotide probes that are biotinylated: wherein the at least 1,000 polynucleotide probes comprise at least 500 different pairs of probes comprising a first probe and second probe, wherein the first probe differs from the second probe and overlaps with the second probe by an overlapping sequence of at least 30 contiguous bases; wherein the polynucleotide probes of the at least 500 different pairs of probes are configured to collectively hybridize to target polynucleotides among cell-free DNA (cfDNA) molecules derived from at least 500 different target genomic regions of the subject, or amplification products thereof; wherein the target genomic regions are human genomic regions; wherein at least one probe for each of the at least 500 different target genomic regions (i) comprises a length of at least 45 bases complementary to a sequence of the target genomic region, and (ii) does not have at least 90% complementarity to at least 45 contiguous bases of 20 or more off-target regions in a GRCh37/hg19 genome; wherein an off-target region does not comprise any of the at least 500 different target genomic regions; and wherein each of the at least 500 different target genomic regions comprises at least five methylation sites and are differentially methylated in cancer samples relative to non- cancer samples.

2. The mixture of claim 1, wherein each of the at least 500 different target genomic regions is determined to be anomalously methylated in cancer training samples based on criteria comprising a number of cancer samples that comprise an anomalously methylated cfDNA fragment that overlaps the target genomic region, and wherein the target genomic region is determined to be anomalously methylated in cancer training samples relative to non- cancer training samples based on criteria comprising Ncancer and Nnon-cancer, wherein:

Ncancer, for each CpG site, is a number of cancer samples that include a cfDNA 02 Oct 2025

fragment covering the CpG site in the cfDNA fragment that (1) has at least 4 CpG sites, wherein at least 70% of the CpG sites are methylated or unmethylated, and (2) has a p-value rarity in non-cancer samples of below a threshold value; and Nnon-cancer, for each CpG site, is a number of non-cancer samples that include a cfDNA fragment covering the CpG site in the cfDNA fragment that (1) has at least 4 CpG sites, wherein at least 70% of the sites are methylated or unmethylated, and (2) has a p-value rarity 2019351130

in non-cancer samples of below a threshold value.

3. The mixture of claim 2, wherein each of the at least 500 different target genomic regions is determined to be anomalously methylated based on criteria positively correlated with Ncancer and negatively correlated with Nnon-cancer.

4. The mixture of claim 2, wherein each of the at least 500 different target genomic regions is determined to be anomalously methylated based on a score, wherein the score is calculated as (Ncancer+1)/(Ncancer+Nnon-cancer+2).

5. The mixture of any one of claims 1 to 4, wherein the cfDNA molecules comprise converted cfDNA molecules produced by converting unmethylated C (cytosine) in the cfDNA molecules to U (uracil).

6. The mixture of claim 2, wherein each of the at least 500 different target genomic regions is either: (i) hypermethylated in the cancer training samples and hypomethylated in the non- cancer training samples; or (ii) hypomethylated in the cancer training samples and hypermethylated in the non- cancer training samples.

7. The mixture of any one of claims 1 to 6, wherein each of the at least 500 different target genomic regions is selected from SEQ ID NOs: 1-131822.

8. The mixture of any one of claims 1 to 7, wherein the at least 500 different target genomic regions comprise at least 40% of the genomic regions in one or more of: (i) SEQ ID NOs: 1-1644, (ii) SEQ ID NOs: 1645-5270, (iii) SEQ ID NOs: 5271-16837, (iv) SEQ ID NOs: 16838-25984, (v) SEQ ID NOs: 25985-46929, (vi) SEQ ID NOs: 46930-67335, (vii) SEQ ID NOs: 67336-101617, or (viii) SEQ ID NOs: 101618-131822.

9. The mixture of any one of claims 1 to 8, wherein the at least 500 different target 02 Oct 2025

genomic regions comprise at least 1,000 genomic regions in one or more of: (i) SEQ ID NOs: 1-1644, (ii) SEQ ID NOs: 1645-5270, (iii) SEQ ID NOs: 5271-16837, (iv) SEQ ID NOs: 16838-25984, (v) SEQ ID NOs: 25985-46929, (vi) SEQ ID NOs: 46930-67335, (vii) SEQ ID NOs: 67336-101617, or (viii) SEQ ID NOs: 101618-131822.

10. The mixture of any one of claims 1 to 9, wherein (i) the mixture comprises 2019351130

converted cell-free DNA (cfDNA) molecules; and (ii) the converted cfDNA molecules comprise deaminated nucleotides obtained by treating cfDNA molecules with a deaminating agent.

11. The mixture of any one of claims 1 to 10, wherein an entirety of the at least 1,000 polynucleotide probes are configured to hybridize to target polynucleotides derived from the at least 500 different target genomic regions.

12. The mixture of any one of claims 1 to 11, wherein (i) each probe of a probe pair further comprises a non-overlapping sequence with respect to the other probe of the pair, (ii) the overlapping sequence of at least 30 nucleotides of a probe pair is complementary to an internal sequence of one of the target genomic regions, (iii) the non-overlapping sequence is at least 15 nucleotides in length, and (iv) the non-overlapping sequences of the probe pair are complementary to different sequences at different ends of the internal sequence.

13. The mixture of claim 12, wherein the non-overlapping sequences are at least 50 nucleotides in length.

14. The mixture of any one of claims 1 to 13, wherein each of the at least 1,000 polynucleotide probes are at least 75 nucleotides in length.

15. The mixture of any one of claims 1 to 14, wherein the at least 1,000 polynucleotide probes together comprise at least 200,000 bases.

16. A mixture comprising an assay panel for enriching target polynucleotides for detecting cancer and/or a type of cancer in a subject; wherein the assay panel comprises at least 1,000 polynucleotide probes that are biotinylated; wherein the at least 1,000 polynucleotide probes comprise at least 500 different pairs of probes comprising a first probe and second probe, wherein the first probe differs from the second probe and overlaps with the second probe by an overlapping sequence of at least 30 02 Oct 2025 contiguous bases; wherein the polynucleotide probes of the at least 500 different pairs of probes are configured to collectively hybridize to target polynucleotides among cell-free DNA (cfDNA) molecules derived from at least 500 different target genomic regions of the subject, or amplification products thereof; wherein the target genomic regions are human genomic regions; 2019351130 wherein at least one probe for each of the at least 500 different target genomic regions (i) comprises a length of at least 45 bases complementary to a sequence of the target genomic region, and (ii) does not have at least 90% complementarity to at least 45 contiguous bases of 20 or more off-target regions in a GRCh37/hg19 genome; wherein an off-target region does not comprise any of the at least 500 different target genomic regions; and wherein each of the at least 500 different target genomic regions comprises at least five methylation sites and are differentially methylated in cancer samples relative to non- cancer samples.

17. The mixture of claim 16, wherein each of the at least 500 different target genomic regions is selected from SEQ ID NOs: 1-131822.

18. A kit comprising an assay panel for enriching target polynucleotides for detecting cancer and/or a type of cancer in a subject; wherein the assay panel comprises at least 1,000 polynucleotide probes that are biotinylated; wherein the at least 1,000 polynucleotide probes comprise at least 500 different pairs of probes comprising a first probe and second probe, wherein the first probe differs from the second probe and overlaps with the second probe by an overlapping sequence of at least 30 contiguous bases; wherein the polynucleotide probes of the at least 500 different pairs of probes are configured to collectively hybridize to target polynucleotides among cell-free DNA (cfDNA) molecules derived from at least 500 different target genomic regions of the subject, or amplification products thereof; wherein the target genomic regions are human genomic regions; wherein at least one probe for each of the at least 500 different target genomic regions (i) comprises a length of at least 45 bases complementary to a sequence of the target genomic region, and (ii) does not have at least 90% complementarity to at least 45 contiguous bases of 02 Oct 2025

20 or more off-target regions in a GRCh37/hg19 genome; wherein an off-target region does not comprise any of the at least 500 different target genomic regions; and wherein each of the at least 500 different target genomic regions comprises at least five methylation sites and are differentially methylated in cancer samples relative to non- cancer samples. 2019351130

19. The kit of claim 18, wherein each of the at least 500 different target genomic regions is selected from SEQ ID NOs: 1-131822.